{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#-*-coding:utf-8 -*-\n",
    "#智联招聘职位数据挖掘分析\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [],
   "source": [
    "#加载数据集\n",
    "df=pd.read_csv('ml_jobs.csv',encoding='utf-8')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 6555 entries, 0 to 6554\n",
      "Data columns (total 15 columns):\n",
      "job_id    6555 non-null int64\n",
      "职位名称      6555 non-null object\n",
      "公司名称      6555 non-null object\n",
      "公司链接      6555 non-null object\n",
      "职位链接      6555 non-null object\n",
      "职位月薪      6555 non-null object\n",
      "工作地点      6555 non-null object\n",
      "发布日期      6555 non-null object\n",
      "工作性质      6555 non-null object\n",
      "工作经验      6555 non-null object\n",
      "最低学历      6495 non-null object\n",
      "招聘人数      6555 non-null object\n",
      "职位类别      6555 non-null object\n",
      "岗位职责描述    6019 non-null object\n",
      "福利标签      5265 non-null object\n",
      "dtypes: int64(1), object(14)\n",
      "memory usage: 768.2+ KB\n"
     ]
    }
   ],
   "source": [
    "#查看数据表信息\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [],
   "source": [
    "#-*-数据清洗与处理-*-\n",
    "df.index=df['job_id']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#删除原文件中'job_id'列\n",
    "del(df['job_id'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [],
   "source": [
    "#对索引列重新排序\n",
    "df_sort = df.sort_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [],
   "source": [
    "#将重新排序后的对象赋予原数据表对象\n",
    "df = df_sort"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>工作地点</th>\n",
       "      <th>职位月薪</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>job_id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>16122</th>\n",
       "      <td>北京-海淀区</td>\n",
       "      <td>25000-50000元/月</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16123</th>\n",
       "      <td>北京</td>\n",
       "      <td>30001-50000元/月</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16124</th>\n",
       "      <td>北京-东城区</td>\n",
       "      <td>50001-70000元/月</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16125</th>\n",
       "      <td>北京</td>\n",
       "      <td>20001-30000元/月</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16126</th>\n",
       "      <td>北京</td>\n",
       "      <td>10000-20000元/月</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16127</th>\n",
       "      <td>北京-朝阳区</td>\n",
       "      <td>50001-70000元/月</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16128</th>\n",
       "      <td>北京</td>\n",
       "      <td>15001-20000元/月</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16129</th>\n",
       "      <td>北京</td>\n",
       "      <td>10001-15000元/月</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16130</th>\n",
       "      <td>北京</td>\n",
       "      <td>12500-25000元/月</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16131</th>\n",
       "      <td>北京-昌平区</td>\n",
       "      <td>12000-24000元/月</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          工作地点            职位月薪\n",
       "job_id                        \n",
       "16122   北京-海淀区  25000-50000元/月\n",
       "16123       北京  30001-50000元/月\n",
       "16124   北京-东城区  50001-70000元/月\n",
       "16125       北京  20001-30000元/月\n",
       "16126       北京  10000-20000元/月\n",
       "16127   北京-朝阳区  50001-70000元/月\n",
       "16128       北京  15001-20000元/月\n",
       "16129       北京  10001-15000元/月\n",
       "16130       北京  12500-25000元/月\n",
       "16131   北京-昌平区  12000-24000元/月"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#查看'工作地点','职位月薪'两列的前10行数据\n",
    "df[['工作地点','职位月薪']].head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [],
   "source": [
    "#用正则表达式处理'职位月薪'列\n",
    "import re\n",
    "#定义三列bottom,top,average分别存放最低月薪、最高月薪和平均月薪\n",
    "df['bottom'] = df['top'] = df['average'] = df['职位月薪']\n",
    "pattern = re.compile('([0-9]+)')\n",
    "q1=q2=q3=q4=0\n",
    "for i in range(len(df['职位月薪'])):\n",
    "    item = df['职位月薪'].iloc[i].strip()\n",
    "    result = re.findall(pattern,item)\n",
    "    try:\n",
    "        if result:\n",
    "            try:\n",
    "                #对于数值如8000-10000元/月的格式，处理后存放至bottom ,top ,average三个新列\n",
    "                df['bottom'].iloc[i],df['top'].iloc[i] = result[0],result[1]\n",
    "                df['average'].iloc[i] = str((int(result[0])+int(result[1]))/2)\n",
    "                #q1统计职位月薪形如'8000-10000元/月'的次数\n",
    "                q1+=1\n",
    "            except:\n",
    "                df['bottom'].iloc[i] = df['top'].iloc[i] = result[0]\n",
    "                df['average'].iloc[i] = str((int(result[0])+int(result[0]))/2)\n",
    "                #q2统计形如月收入'10000元/月以下'的次数\n",
    "                q2+=1\n",
    "        else:\n",
    "            #对于字符如'面议','无内容'的格式,处理后保持原字符不变\n",
    "            df['bottom'].iloc[i] = df['top'].iloc[i] = df['average'].iloc[i] = item\n",
    "            #q3统计'无内容','面议'的次数；\n",
    "            q3+=1\n",
    "    except Exception as e:\n",
    "        #q4统计特殊情况的次数\n",
    "        q4+=1\n",
    "        print(q4,item,repr(e))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>职位月薪</th>\n",
       "      <th>bottom</th>\n",
       "      <th>top</th>\n",
       "      <th>average</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>job_id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>16122</th>\n",
       "      <td>25000-50000元/月</td>\n",
       "      <td>25000</td>\n",
       "      <td>50000</td>\n",
       "      <td>37500.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16123</th>\n",
       "      <td>30001-50000元/月</td>\n",
       "      <td>30001</td>\n",
       "      <td>50000</td>\n",
       "      <td>40000.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16124</th>\n",
       "      <td>50001-70000元/月</td>\n",
       "      <td>50001</td>\n",
       "      <td>70000</td>\n",
       "      <td>60000.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16125</th>\n",
       "      <td>20001-30000元/月</td>\n",
       "      <td>20001</td>\n",
       "      <td>30000</td>\n",
       "      <td>25000.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16126</th>\n",
       "      <td>10000-20000元/月</td>\n",
       "      <td>10000</td>\n",
       "      <td>20000</td>\n",
       "      <td>15000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16127</th>\n",
       "      <td>50001-70000元/月</td>\n",
       "      <td>50001</td>\n",
       "      <td>70000</td>\n",
       "      <td>60000.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16128</th>\n",
       "      <td>15001-20000元/月</td>\n",
       "      <td>15001</td>\n",
       "      <td>20000</td>\n",
       "      <td>17500.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16129</th>\n",
       "      <td>10001-15000元/月</td>\n",
       "      <td>10001</td>\n",
       "      <td>15000</td>\n",
       "      <td>12500.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16130</th>\n",
       "      <td>12500-25000元/月</td>\n",
       "      <td>12500</td>\n",
       "      <td>25000</td>\n",
       "      <td>18750.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16131</th>\n",
       "      <td>12000-24000元/月</td>\n",
       "      <td>12000</td>\n",
       "      <td>24000</td>\n",
       "      <td>18000.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  职位月薪 bottom    top  average\n",
       "job_id                                       \n",
       "16122   25000-50000元/月  25000  50000  37500.0\n",
       "16123   30001-50000元/月  30001  50000  40000.5\n",
       "16124   50001-70000元/月  50001  70000  60000.5\n",
       "16125   20001-30000元/月  20001  30000  25000.5\n",
       "16126   10000-20000元/月  10000  20000  15000.0\n",
       "16127   50001-70000元/月  50001  70000  60000.5\n",
       "16128   15001-20000元/月  15001  20000  17500.5\n",
       "16129   10001-15000元/月  10001  15000  12500.5\n",
       "16130   12500-25000元/月  12500  25000  18750.0\n",
       "16131   12000-24000元/月  12000  24000  18000.0"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#查看数据表中'职位月薪'与新增加3列的前10行数据\n",
    "df[['职位月薪','bottom','top','average']].head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6555\n"
     ]
    }
   ],
   "source": [
    "#查看总统计分析样本数\n",
    "print(q1+q2+q3+q4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [],
   "source": [
    "#使用正则表达式进行'工作地点'列的处理，新增工作城市列，\n",
    "df['工作城市'] = df['工作地点']\n",
    "pattern2 = re.compile('(.*?)(\\-)')\n",
    "df_city = df['工作地点'].copy()\n",
    "for i in range(len(df_city)):\n",
    "    item = df_city.iloc[i].strip()\n",
    "    result = re.search(pattern2,item)\n",
    "    if result:\n",
    "        #将工作地点中如'北京-海淀区','北京-东城区'等转化为'北京'存放在工作城市列\n",
    "        df_city.iloc[i] = result.group(1).strip()\n",
    "    else:\n",
    "        #对于字符如'北京','无内容'的格式,处理后保持原字符不变\n",
    "        df_city.iloc[i] = item.strip()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#新增加1列'工作城市'到数据表中\n",
    "df['工作城市'] = df_city"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>工作地点</th>\n",
       "      <th>工作城市</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>job_id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>16122</th>\n",
       "      <td>北京-海淀区</td>\n",
       "      <td>北京</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16123</th>\n",
       "      <td>北京</td>\n",
       "      <td>北京</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16124</th>\n",
       "      <td>北京-东城区</td>\n",
       "      <td>北京</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16125</th>\n",
       "      <td>北京</td>\n",
       "      <td>北京</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16126</th>\n",
       "      <td>北京</td>\n",
       "      <td>北京</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16127</th>\n",
       "      <td>北京-朝阳区</td>\n",
       "      <td>北京</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16128</th>\n",
       "      <td>北京</td>\n",
       "      <td>北京</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16129</th>\n",
       "      <td>北京</td>\n",
       "      <td>北京</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16130</th>\n",
       "      <td>北京</td>\n",
       "      <td>北京</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16131</th>\n",
       "      <td>北京-昌平区</td>\n",
       "      <td>北京</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16132</th>\n",
       "      <td>北京</td>\n",
       "      <td>北京</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16133</th>\n",
       "      <td>北京-大兴区</td>\n",
       "      <td>北京</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16134</th>\n",
       "      <td>北京-朝阳区</td>\n",
       "      <td>北京</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16135</th>\n",
       "      <td>北京-大兴区</td>\n",
       "      <td>北京</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16136</th>\n",
       "      <td>北京</td>\n",
       "      <td>北京</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16137</th>\n",
       "      <td>北京-朝阳区</td>\n",
       "      <td>北京</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16138</th>\n",
       "      <td>北京-海淀区</td>\n",
       "      <td>北京</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16139</th>\n",
       "      <td>北京</td>\n",
       "      <td>北京</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16140</th>\n",
       "      <td>北京-朝阳区</td>\n",
       "      <td>北京</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16141</th>\n",
       "      <td>北京</td>\n",
       "      <td>北京</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          工作地点 工作城市\n",
       "job_id             \n",
       "16122   北京-海淀区   北京\n",
       "16123       北京   北京\n",
       "16124   北京-东城区   北京\n",
       "16125       北京   北京\n",
       "16126       北京   北京\n",
       "16127   北京-朝阳区   北京\n",
       "16128       北京   北京\n",
       "16129       北京   北京\n",
       "16130       北京   北京\n",
       "16131   北京-昌平区   北京\n",
       "16132       北京   北京\n",
       "16133   北京-大兴区   北京\n",
       "16134   北京-朝阳区   北京\n",
       "16135   北京-大兴区   北京\n",
       "16136       北京   北京\n",
       "16137   北京-朝阳区   北京\n",
       "16138   北京-海淀区   北京\n",
       "16139       北京   北京\n",
       "16140   北京-朝阳区   北京\n",
       "16141       北京   北京"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#查看数据表中'工作地点'与新增加列前20行数据\n",
    "df[['工作地点','工作城市']].head(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "北京      2246\n",
       "上海       835\n",
       "深圳       651\n",
       "杭州       318\n",
       "广州       293\n",
       "成都       261\n",
       "南京       251\n",
       "武汉       195\n",
       "西安       168\n",
       "苏州       131\n",
       "无内容      122\n",
       "济南       112\n",
       "郑州       108\n",
       "合肥       108\n",
       "天津       103\n",
       "长沙       100\n",
       "厦门        85\n",
       "福州        78\n",
       "大连        70\n",
       "重庆        55\n",
       "青岛        45\n",
       "石家庄       33\n",
       "佛山        32\n",
       "贵阳        30\n",
       "太原        25\n",
       "昆明        25\n",
       "无锡        24\n",
       "南宁        16\n",
       "宁波        13\n",
       "南昌        10\n",
       "海口         7\n",
       "呼和浩特       5\n",
       "Name: 工作城市, dtype: int64"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#-*-数据分析-*-\n",
    "\"\"\"\n",
    "对于'机器学习'这个关键词的6555条招聘数据进行数据分析，由于招聘数据与工作城市、工作经验、最低学历和平均月薪等特征密切相关,\n",
    "所以选取数据集中工作城市、工作经验、最低学历和平均月薪4个特征列进行分析。\n",
    "\"\"\"\n",
    "#1.不同城市的招聘数量分布情况分析\n",
    "#统计每个城市的招聘数量\n",
    "df['工作城市'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6555"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#汇总全部城市的招聘数量\n",
    "df['工作城市'].value_counts().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [],
   "source": [
    "#处理干扰数据，将原来df['工作城市']列中显示'无内容'的项替换成空值nan\n",
    "df_city = df['工作城市'].replace(['无内容'],np.nan)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "31"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#统计有效城市数量\n",
    "df_city.value_counts().count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#将新的[df_city]列添加到数据表中\n",
    "df['df_city'] = df_city"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6433"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#统计有效职位数\n",
    "df_city.value_counts().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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AMrdzXrp0yfjUq1fPWK126dIlfvrpJ7y8vP5SPMnJyfj5+bFv3z4mTJhAmzZt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\nRnR0NN9//z0zZsx45LMmTZrESy+9lOO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zAAAg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gpgk2CZnMLV14PjvvtQDht7UqxeUkLo9s8SXjWNQ/YnabI9RFzKrfH26enLaQh8icJ4HcmO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\npcLCQn366afas2dPUBGDgYEB7dy5U4cOHZLL5QqYIQcAAP4bCN4AA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      "text/plain": [
       "<matplotlib.figure.Figure at 0x12a48908>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#-*-数据可视化-*-\n",
    "matplotlib.style.use('ggplot')\n",
    "#设置视图画布1\n",
    "fig1 = plt.figure(1,facecolor = 'white',figsize=((15,10)))\n",
    "#设置子图1\n",
    "ax1 = fig1.add_subplot(2,1,1)\n",
    "#设置轴的颜色为黑色\n",
    "plt.tick_params(colors='black')\n",
    "#画柱状图\n",
    "df_city.value_counts().plot(kind='bar',rot=0,color='#7EC0EE',fontsize=8)\n",
    "#设置图标题，x和y轴标题\n",
    "title = plt.title('城市—职位数分布图',fontsize=12,color='black')\n",
    "xlabel = plt.xlabel('城市',fontsize=10,color='black')\n",
    "ylabel = plt.ylabel('职位数量',fontsize=10,color='black')\n",
    "#设置说明，位置在图的右上角\n",
    "text1 = ax1.text(25,2400,'城市总数:31(个)',fontsize=10, color='black')\n",
    "text2 = ax1.text(25,2200,'职位总数:6555(个)',fontsize=10, color='black')\n",
    "text3 = ax1.text(25,2000,'有效职位:6433(个)',fontsize=10, color='black')\n",
    "#统计有效招聘数量\n",
    "list_1=df_city.value_counts()\n",
    "#添加每个城市的坐标值\n",
    "for i in range(len(list_1)):\n",
    "    ax1.text(i-0.3,list_1[i],str(list_1[i]),color='black')\n",
    "#添加栅格线\n",
    "plt.grid(True)\n",
    "#设置子图2，是位于子图1下面的饼状图\n",
    "ax2 = fig1.add_subplot(2,1,2)\n",
    "#x是数值列表，\n",
    "x = df_city.value_counts().values\n",
    "#label_list是构造的列表，存储前8个城市名称+职位占比\n",
    "label_list = []\n",
    "for i in range(8):\n",
    "    t = df_city.value_counts().values[i]/df_city.value_counts().sum()*100\n",
    "    city = df_city.value_counts().index[i]\n",
    "    percent = str('%.1f%%'%t)\n",
    "    label_list.append(city+percent)\n",
    "#显示前8个城市的城市名称和比例、其余的不显示，用空字符列表替代，为此需要构造列表label_list和一个空字符列表['']*23\n",
    "labels = label_list + ['']*23\n",
    "#explode即饼图中分裂的效果explode=（0.1，1，1）表示第一块图片显示为分裂效果\n",
    "explode = tuple([0.1]+[0]*30)\n",
    "colors='#FF8247','#ADD8E6','#FF3030','#7FFF00','#C67171','#9ACD32','#9400D3','#8B864E'\n",
    "#饼图的比例根据数值占整体的比例而划分,通过构造labels手动显示饼图中每一块的比例\n",
    "plt.pie(x,explode=explode,labels=labels,textprops={'color':'black'},colors=colors)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "78.5014767604539\n"
     ]
    }
   ],
   "source": [
    "# 上图统计出排名靠前的8个城市提供的工作机会占比超过78%，说明提供机器学习相关岗位工作机会的城市主要集中在一线城市和新一线城市中\n",
    "chance_num=0.0\n",
    "for i in range(8):\n",
    "    chance_num+=df_city.value_counts().values[i]/df_city.value_counts().sum()*100\n",
    "print(chance_num)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "不限       2025\n",
       "3-5年     1900\n",
       "1-3年     1610\n",
       "5-10年     593\n",
       "无经验       200\n",
       "无内容       122\n",
       "1年以下       64\n",
       "10年以上      41\n",
       "Name: 工作经验, dtype: int64"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#2.不同工作经验的招聘数量分布情况分析\n",
    "#统计不同工作经验的招聘数量\n",
    "df['工作经验'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [],
   "source": [
    "#处理干扰数据，把'工作经验'中'无内容'项替换成空值nan\n",
    "df_experience=df['工作经验'].replace(['无内容'],np.nan)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "不限       2025\n",
       "3-5年     1900\n",
       "1-3年     1610\n",
       "5-10年     593\n",
       "无经验       200\n",
       "1年以下       64\n",
       "10年以上      41\n",
       "Name: 工作经验, dtype: int64"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#统计处理后的数据\n",
    "df_experience.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6433"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#统计处理后的工作经验样本数\n",
    "df_experience.value_counts().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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2cHBwgKGhIW7evCnn9ff3x+nTp7Ft2za5S1H2GAAASExMRHh4OC5evIikpCTo6emhUqVK\nsLS0RGBgILy9vWFoaIg///wTHTp0wK5du+R+/zY2NvKXZ1U9fvwYkiShYsWKMDc3l7sqZWZmol27\ndmjXrh1u3ryJKlWqyO9l/PjxcgCV39iANm3aoF+/foiJiZG7Wh06dAidOnVSCticnZ3lL9eSJKFu\n3boAsgI4MzMzeRXsnj17Yt68ebh79y7q1q2LUqVK5Xnv69evw8bGJkdAAWR16TIwMFA6lj0b05w5\ncz5oTElusrtgxcXF4ccff8SPP/6IJ0+e4MqVK7h8+bI80xcRkaoYVBARqTGRx5iKadOmYdy4cXj0\n6FGOcQIAlL7om5qaQk9PD5UrV86Rr2bNmrh+/ToqVqwIV1dXWFhYyF/Ou3Tpgq1bt+KXX35B48aN\nERQUhCJFiiA1NVUeh/Axiay1k5CUlIQTJ07gyJEjuH//PkqUKIEnT57g2rVrcHFxwfnz51G7dm14\neHigdevWBVqV2t3dHUIIHDx4EL169cKLFy9w5swZTJ48WSnf/fv35We+deuWPLVr//79kZqaKufr\n2bMnRo8ejfXr12Pjxo353nvbtm3ydLHvSkxMzBFUdOvWDV27dn3vMxXUrVu3YGdnBzMzM9ja2sLW\n1hb29vZwdXUtUEsZEVFBMKggIlJjkiTlu05FdkBx8+ZNnDt3DmfOnEH//v3lX9jfZ8mSJbCzs5N/\nsZ40aZL8Jfe7777D7t27kZSUhPHjx0NLSwszZ85E9+7doaur+xGe7v/FxcVh8uTJkCQJlStXho6O\nDkqXLg1jY2MEBARAX18fY8aMgbu7O5ydnQFkBVZeXl7w8vJCnTp18r2+qakpJkyYgIoVKwLIauEx\nMTFB48aNc+RdsWIF9PX1sX//fnTv3h179+7Fzp075SllAUBXVxeWlpa4fft2vvfeu3cv7t27J69D\n8ra0tDSkpqbm6P4EoECzaqWlpUFLSwuSJOHWrVt5ttRUrlwZN27cUNpWr16N27dvQ6FQ4LvvvsO2\nbdveez8iovwwqCAiKqSXL1/i1atXuaY9ffoUQO5dl7JVqlQpz7S35TWm4u+//4aPjw/CwsKQlpaG\n2rVro169eihbtmyO8/PSoEEDpf34+Hi5O5GTk5O85oOTkxOeP3+Offv25Zhh6GMwNDTE06dPMX36\ndDRq1AjOzs7Yvn07ZsyYgdatW2Pbtm04c+YMrl69Kp/Ts2dPLFu2DN7e3jh//vx77zFx4kT589at\nW9GhQ4dcZ7cKDg7Gli1bMGXKFPz66694/vw51qxZIwck2WNZoqKiUL58ebRp0wZnz55VmsEKAG7c\nuIGBAweiR48eqFWrVo77XL16Ve7uVRirVq2Ct7c3tLS0kJmZCX9//1zzaWhoyC0UHTt2lI8nJibi\n8uXLKFasmPxcRESF9nnGh+fu9u3bwtPTU1haWgojIyPh7u4u7ty5I4QQYt26dTlmBXl7pgshsmYH\nsbOzE3p6esLZ2VlcvnxZKf3EiROidu3aQk9PT1StWlUcPXr0P3s2Ivr6+Pr6yv8uFWa7e/euEEK8\nd/anvGaQio2NFTNmzBCnT58WaWlpQgghIiMjc/xbmX2/d2dc2rVrlxBCiEOHDonz58+LM2fOCGtr\nazFixIh8Z2rKvlaFChU+0ZvNsmnTJmFnZydevHghzMzMhK+vb448ly5dEhoaGkqzL2XP/nT8+PEC\nPUf2/oQJE+RrnDhxQjRp0kSYmJgIS0tLUapUKbF8+XKRnJwsPD09hY6Ojjhy5IiIiIgQZmZmwtbW\nVun/nD179ggzMzNRp04deSauS5cuib/++kuEhYWJ06dPiwYNGohq1arl+w4WLFggWrRooXQse/an\n5ORkERERIW7fvp1jxqaQkJBCz8TF2Z+IqDDUqqVi9OjRqFq1KkaNGoXk5GSMGjUK7du3xz///AMA\nKFu2LE6cOCH/mqKnpyefe/bsWXh6emL+/Plo3LgxJk+ejNatW+PevXswMDBAZGQk2rRpA29vb6xf\nvx6///47OnXqhJs3byrNCf+1CAwMVJpHndQH60a9fUj9+Pn5wc/PT+V7FnYBOWNjY4wbN07pmIWF\nhdIg7fxYWFgAyFoBeu/evdDQ0ECjRo0wfvx4/PTTT+89P7eBx5/CvXv34ODgADs7uxxptWvXxuDB\ng3OsRA1AXnOioExNTTF69Gjs27cPkZGRGDBgAHbu3AlDQ0MsWLBAXgTv7t272L59O1q1agUgawG9\n9u3bo2HDhrh//z68vb2xc+dOeHp6Yvny5fKK5KGhoRgzZgwyMzOhqakJR0dHbN68Od8y+fj4wMfH\nJ9c0PT29fFs5dHV18ffffxe4BeLx48do3rx5gfK+i/+uqTfWj/r6qurmc0c1b3vx4oXS/oULF4SG\nhob4999/xbp16/L9Vaxz586iU6dO8n5cXJzQ09MT69atE0II4ePjIxwdHeX0jIwMUbZsWTF58uSP\n/BTqoV27dp+7CJQH1o16+1brJzMzM88WEXXxX9TN8OHDhb+/v3j58mWOtLS0NNGvXz9x/fr1HGkv\nXryQW5vCw8PFyZMn871PfmuPfIm+1b83XwrWj/r6mupGrVoqTE1NlfazB69lr66an+DgYMyaNUve\nNzIyQq1atXDu3Dl4eXkhJCREafYNTU1NuLm54dy5cx+p9EREX668Vs7+1mRPlZsbbW1tBAQE5Jpm\namqKfv36AciaUet9Cts6RUSkrtT6f5Hdu3fD0tIS9vb2AIAHDx6gSJEisLW1hY+PDxISEgBkzRoS\nFxeHChUqKJ1frlw5PH78GEBW83l+6UREREREVDhq1VLxtmvXrsHPzw+bN2+GJEnw8PCQp/O7ePEi\nxo8fj7t372L//v3yaqrvzvVtYGAgz8yS21zgBgYGSElJ+Q+ehoiIiIjo66WWQcWjR4/Qpk0bDB06\nVJ7+ztzcHObm5gCAGjVqwMjICN26dcPTp0/l+dLT0tKUrpOSkiIHErq6uvmmf22sra0/dxEoD6wb\n9cb6UV+sG/XFulFvrB/19TXVjSSEek1M/ezZMzRq1Aj169fPs+8qANy+fRtVqlTBpUuX4OjoCAMD\nA6xYsQK9e/eW87i5ucHJyQkLFiyAjY0NevbsqTRPee/evZGYmIjdu3fneo/AwEAEBgYqHXNzc8PI\nkSNVfEoiIiIioi/DvHnzEBoaqnTM09NTaeYqtQoqXr16hcaNG6NWrVpYv359vnk3bdqEvn37IiYm\nBkWLFkXTpk1hYWGBjRs3AshawKlMmTLYsWMHWrdujX79+uHOnTs4efIkgKzB31ZWVvD19S3Q1Inv\nio2NRUZGxoc/5H+kWLFi8pgTUi+sG/XG+lFfrBv1xbpRb6wf9aXudaOlpYXixYsXLO8nLkuBJSQk\noEWLFihRooQ8XiKblZUVZs2aJa8IGhYWhlGjRmHIkCHyyq8+Pj7o0qULGjZsiHr16mHKlCmoUqUK\nPDw8AABDhw5F3bp1MW3aNHTu3BlLly6FEAJeXl6FKm9GRgbS09NVf/BPRAih1uX7lrFu1BvrR32x\nbtQX60a9sX7U19dUN2oTVISHh+Pq1asAIC9wJISAJEm4f/8+dHR0MHToUMTFxcHKygojRoxQ6obU\nrl07LFq0CNOmTUNsbCyaNWuGAwcOyNP21axZE4GBgfD19YWfnx/q1KmDY8eOydPWEhERERFR4ahV\n96cvyYsXL9Q6sjQxMUFMTMznLgblgnWj3lg/6ot1o75YN+qN9aO+1L1utLW1YWZmVqC8ar1OBRER\nERERqT8GFUREREREpBIGFUREREREpBIGFUREREREpBIGFUREREREpBIGFUREREREpBIGFURERERE\npBIGFUREREREpBIGFUREREREpBIGFUREREREpBIGFUREREREpBIGFURERETfuMzMTKV9hULxn9w3\nOTn5P7kPfXoMKoiIiIg+ooSEBPnzkydPCvTFOSQkBPv37/8o9xdCoGHDhli1alWu6Y8ePUK9evXk\n/QcPHqBVq1ZISkqSj02dOhXLly/P9z4XL15Es2bN8s0TERGBjIwMREdHw8nJCQDw8uVLxMfHAwC6\ndu2KgwcPFui51F1ycjJu3boFIcTnLspnwaCCiIiI6B0xMTF48eJFgba3g4hLly6hTp06SE1NBQB8\n//33OH78+HvvN23aNLl1oEePHihfvjysrKzkrVy5cmjTpo2c38nJCVWrVoW9vT0sLS3x+PFjOW3/\n/v1ISEjAli1b8mxxkCRJ/ly+fHmYmZlh4sSJAIDo6Ghs3boVDRo0eG+5375ObmbNmoUVK1Yo5R0z\nZgw2bNiAjIwM3LhxAzVq1HjvfQBg7dq1cHNzg7W1NerWrYu7d+/myPP48WPY2NgolV2hUGDUqFFw\ncnJCpUqV0LZtW1y4cCFHOV1cXFCxYkU0b94cR48ezXHtsLAwODs7K9X323755Re0aNEC165dyzU9\n+8/G69evC/S8Xxqtz10AIiIiInXTrl07PHz4UOlY9i/Q736RbtWqFVavXg0A2LZtG1q0aAFdXV2c\nPHkSr169QsuWLfO918GDB6Grq4uOHTsCADZv3qyUHhcXh06dOqFHjx7ysUuXLsmfa9SogeLFiwMA\n3rx5g+nTp2PRokXYtWsX/P39MWXKlFyf423Tp0/HX3/9BQDw8/NDjx494ODgIKe/2z0KADIyMvJM\n09DQgCRJGDZsGLp37462bdtCCIHQ0FDcuXMHy5cvx/nz56Gvr4+yZcvm+34AYPbs2di6dSsmTpwI\nBwcHREZGwtDQMEc+X19fmJqa5ihneno6li5dCgMDA/j7+6NPnz44deoUTExMAGS1nsyZMwclSpTA\nli1bMHDgQBw9ehS2trbydUaPHo0pU6agWLFiOe4bGBiIs2fPok+fPhgyZAiOHTsGfX19pTxOTk5o\n2bIlZs6cCT8/v/c+85dGEt9qG42KXrx4gfT09M9djDyZmJggJibmcxeDcsG6UW+sH/XFulFf30rd\n9O/fHw4ODvDx8ck1PSkpCY6Ojli1ahXc3NwwYMAA/Pnnn3JrgZaW8m+5I0aMQL9+/dCiRQv89ttv\n8pfuESNGyHliY2Ph5eUFJycnuSUBAK5fv46DBw9i9OjRqFSpEiIiIgAAw4YNAwAsXLgQcXFxaNmy\nJVavXo3q1avj2bNn6N69O9LT0xEVFYWKFSuiTZs2WLZsmRwoCSGQnJwMPT09aGhooEWLFvD19UW9\nevVyBFNvf4V8N61x48bYuHEjgKzuVdra2ujQoQNCQ0Nx9+5dODg4wNfXF/v370etWrXk84oXLw5/\nf3+la0VERKBZs2bYsWMH6tSpk1f1YN++fVi8eDFatWqF/fv349SpU7nmi4mJQfXq1bF27Vq0aNEi\nR7oQAnZ2dhgzZgz69u0LADh+/DimTJmCkydP5sh/7NgxDBo0CKtXr0aTJk3QrVs3CCGwfv36HIFF\nVFQUmjdvjnPnzqF48eJq/3dHW1sbZmZmBcrLlgoiIiKij2DDhg1ISkqCs7MzQkJCcPr0aZw5cwaB\ngYFISEjA5MmTc5wzc+ZM1KtXD05OTmjTpg2+++47Oe2ff/7BwIEDERcXh6lTpyqdd+/ePbx48QKv\nX79G0aJFAQCLFy/G9evXsW/fPgCAsbGx/Kv84sWLUb9+fWzbtg27du1CQEAAtm3bBkNDQ6UgqUGD\nBli+fLlSKwWQNQ7jbfHx8WjatClevHiBn376Cb6+vjme7cqVK/KX8jdv3iAlJQWurq6QJAk9evTA\nnj170LFjRzg6OiIpKQkTJ07E4sWLkZycDDc3NwwcOBADBgzAjh07UK1atXwDitjYWEyZMgUrV65E\naGhonvmA/29ZyW7deZdCoYBCoVBK37t3L7p06ZIj77Zt2zBu3Dj4+fmhadOmAICAgAB07doV3bt3\nx++//47SpUvL+S0tLVGzZk0cOnRIqeXpa8AxFUREREQqevHiBRYtWgQga8DuuHHjMGbMGPkLZV4d\nQ/744w8cOnQIVatWhSRJ6NOnD549e4aJEyfC09MTo0ePxtSpU+VuNdHR0fL9SpQogbi4OBgbG2PN\nmjXYtm0bBg0ahMuXL+PkyZM4efIk0tLS4Ovri//973948OABTE1NERwcDE1NTblF4m1CiALN/DRl\nyhQ0bdq6K+j/AAAgAElEQVQUlSpVwvnz53Hr1q0ceWrWrIk///wTnp6ecHJygrGxMZo1a4YVK1Yg\nLS0NaWlpqFixIrp164YGDRrA3NwcnTt3hpaWFipVqgRzc3MAQHh4OOzt7TF16lTUqFEDDRs2lMdp\nZJs6dSrc3d3lweB5efz4McaPH4/69evnmvfly5eYOHEiLC0t4e7uLh8/f/48XFxc5P3sOh43bhzm\nzZuHrl274vbt29iwYQOKFCmCrVu3okiRImjRogW2bNkidxUDgHr16uH8+fPvfcdfGgYVRERERCqa\nMWOGPDhYS0sLvXv3Rp8+feT0DRs2wNbWFra2trCxsZG7/AQFBeHQoUPQ1NTE3Llz8fvvv8PV1RVx\ncXE4evQoOnTogC5duiA0NBSamppo3Lgx/v33Xzx//hwmJiaIjY2FkZERmjRpgj179iA4OBibNm3C\nxo0bsWnTJmzatAkaGho4fvw4rKysEB0djcjISCQkJKBLly4ICQlBhw4dYGNjAxsbGzx8+BBdunSB\njY0NbG1tceLEiRzPum3bNty/fx8//PADNDQ0MGHCBAwZMkRp9igACA0NRbt27VC+fHmMHTsWzZs3\nx4QJE3DkyBE0atQIfn5++OeffwAA165dg52dHYCsLjeBgYFo3749AOD58+cICgqCjo4ONm3aBC8v\nL8ycORM7duwAADmAGj9+fJ71s2vXLlhZWaFu3bp4/Pgx5s2bp5R+/vx5VKhQATVr1sSJEyewaNEi\nOeBKS0tDdHQ0KlasCCCrBalx48Y4ceIEdu3ahU6dOsnHly1bBgAoWrQoNm7ciIEDB2LChAlKrRwV\nKlTAgwcP8izrl4rdn4iIiIhUlJSUBD8/Pxw+fBja2toYNGiQUrqXl1eu3Z90dXUxbNgwDBo0CHZ2\ndjAwMICHhwesra2V8hUrVgyLFy/GmTNnYG9vj8DAQGzcuBGLFi1CWloaNmzYgHHjxmH27Nl4/vw5\ntm7dirFjxwIAtm/fLvftnzt3Lrp27Yrt27dj9erV+PXXX7F37155TET9+vWxYsWKHN2fsp09exaz\nZ8/GgQMH8OTJEwBArVq10LJlSwwePBhr1qyBpqYmAKBhw4b466+/kJCQgAYNGuD8+fMwMjLChAkT\nAAA2NjaYM2cO0tPTERISojTN7dsyMjJgY2Mjd7GqVq0aLl68iJ07d6Jdu3bw9fXF1KlT5YHbubUK\ntWrVCtWrV8eLFy+wd+9etGzZEtu3b0e1atUAZA12DwoKQmxsLI4fP44OHTpgxYoVaN68OeLi4iBJ\nEoyNjQEAZcuWRceOHTFs2LAcYybe5e3tjY4dOyIqKko+ZmJigri4uHzP+xKxpYKIiIhIRStWrEDx\n4sXznGI1r+5PkydPlqdGHT16NM6dO4e5c+fKU8paWlqiXLly8n72zEPTpk3D9evXcf36dYwfPx66\nurrw9/fH/PnzUaJECaxduxYpKSmIjIzEpEmToK+vjwcPHuD06dNo164dAKBq1arYs2dPjjLn1f3p\n4sWLGDBgAH777TdYWFgopY0cORKZmZno27evPJ3ugQMHUK9ePTRu3BhpaWlo2rQpHB0d4ejoiKdP\nn8LU1BRWVlZYtmwZjh49KrdMvCs739usra3x8uVLHD58GA8fPoSPj4/c2rJkyRI8ePAAtra2cuBT\npEgRVK5cGa6urpgzZw6qVq2KlStXytfT09NDpUqV4OzsjLFjx6Jdu3ZYsmQJgKyWEyCrxQLIGqsy\nduzY9wYU2SwtLeHq6irvp6SkQEdHp0DnfknYUkFERESkovet17Bu3Tps2rRJ6VhoaCiMjY3RvHlz\nSJKEJk2aoHbt2ujataucx9PTE71794aHh4d8LD4+HkFBQfjnn39w9epVVKlSBSVLloS7uzu6d++O\nCRMmwM7ODmfOnEF4eDi+++476Ovrw8LCAjNmzFAaR9G2bVvcuHFD3k9PT5e782Q7c+YMrl27hp9/\n/hnTpk1Do0aNACgHShoaGli1ahW6deuGjh07YunSpWjfvj1sbGzQp08fnDt3DkWLFsXly5cxatQo\nlCxZEgDw448/YsCAAfDw8ED58uVzfXdOTk45Bl/fvn0b1tbWaNWqVY5ZngICAnDs2DFs374dpUqV\nyvWampqauU6Fm01LS0tONzY2hqamJl6+fIly5crleU5BvXr1Sp7K9mvCoIKIiIjoE+vbt2+u3Z9G\njhwpf378+DEiIyPlAcpA1hf3d1s5EhMT8ffff6N27dro378/du/eDV1dXVSpUgXFihXDhQsX0LVr\nV6SlpWHnzp3YsmULgKwvys2bN1eayemPP/5QunZu3Z8iIiIwcOBAzJ49G2/evIGNjQ0kSUJmZibS\n09Nha2sLIQRKlCiBv/76C97e3liwYAGWLFmC6Oho6OrqYsqUKejYsSPGjBmDOXPmyEFYfHw8JElS\n+tU/PT0dXl5e6NatG9q3bw8vLy9s2LABEydOxA8//ICTJ0/i+PHj2LVrFwwMDHIEI8bGxtDS0pID\ngL179+Lp06eoW7cuNDU1sXfvXpw7d04O8k6dOoWzZ8+icePGMDAwQHBwMHbu3InZs2cDyAoY7e3t\nER4e/lGCivDwcLnb1deEQQURERHRW5KSkpCSkpLjeFpaGpKSknJdV8DAwEDuJvMh/P39cfHiRdy+\nfRslS5ZEx44dc0yd+m5QYWFhoTTF7Js3b+T+/tu2bUPJkiVRt25dnDlzBkWKFEHt2rU/aC2Ed+9X\nqVIlhIaGyl2evLy8AGR1hxo7dmyOFcNXr14t/8rfpEkT1K9fHwMGDEC3bt1gbW0NAwMDAFnrO4wf\nPx5Tp06Fv78/Jk6ciEmTJiEjIwN37tzBs2fPAGR1H9q4cSMmTZqEzZs3o2zZsli6dOl7Z3rKZmlp\niYCAACxcuBBaWlqws7PDli1bUL9+fQBAmTJlcPHiRaxbtw4KhQKVKlXC0qVLlVYwd3NzQ3BwMDp0\n6FDg95ib7LVI5syZo9J11BGDCiIiIqK3jB8/Hjt27Mi1S1NISAiWL1+e4/ivv/6KAQMG5Dl2Ii/l\nypVDnTp1UKtWLWhra2P8+PEoX768fG+FQoGzZ89CkiQIISBJEk6fPo0iRYrgzZs3KFGiBMLCwvD0\n6VPMmDFDaRG7zMxMKBQKefGyt6d9/dByvjuG4n00NTURGRmJPXv2YO/evShdujQOHz6M06dPY968\neXBycsLSpUsxf/58dOjQAc7OzujVqxeuXbuG3bt34+LFi0rXq1u3Lo4cOVKgew8fPhzDhw+X92vX\nro0DBw7kmd/a2hrbt2/P95q9evVC06ZNMXHiRJW6Lh09ehS6urpwc3Mr9DXUFYMKIiIiorf89ttv\n+O233z74vMzMzPeOrXjXu798z5gxAzNmzHjveREREWjatCkkSYKzszPmzZuHxYsX55o3t1Wb8yrn\nh5Y/P8+fP0dCQgKWLl0qd6eqVq0aBg0ahKlTp2Lnzp2oWbMmAMDBwQFHjx5FSEjIRy3Dx2JhYYHu\n3btj1qxZhW5lSEtLw5w5c/Kd+vZLJokPDVUJQNaiM+np6Z+7GHlS92Xfv2WsG/XG+lFfrBv1xbpR\nb6yfjyM1NRUrVqxAv379UKRIkQ8+/8aNGwgPD0f37t3lY+peN9ra2nJL1/uwpYKIiIiI6D10dXUx\ndOjQQp9vZ2cnL/D3NeI6FUREREREpBIGFUREREREpBIGFUREREREpBIGFUREREREpBIGFURERERE\npBIGFUREREREpBIGFUREREREpBIGFUREREREpBK1Ciru3LmD7t27o1y5cjA2NoaHhwciIiLk9GXL\nlsHa2hoGBgZo1qwZ7t+/r3T+rl27YG9vD319fdSpUwdhYWFK6aGhoXBycoK+vj4cHBxw7Nix/+S5\niIiIiIi+ZmoVVIwePRrW1tbYt28fDh06hISEBLRv3x4KhQLbt2/H8OHDMX36dJw+fRrp6eno2LGj\nfO7Zs2fh6emJwYMH48KFC7C0tETr1q2RlJQEAIiMjESbNm3QsmVLXLp0CY0aNUKnTp0QFRX1uR6X\niIiIiOirIAkhxOcuRLaXL1/C1NRU3r948SLq1auHf/75Bz179kTjxo0xf/58AMDNmzdhb2+P4OBg\nNGrUCF26dIEQArt37wYAxMfHo1SpUli+fDm8vLwwfPhwhISEyK0XmZmZsLKywoABAzBp0qQPLuuL\nFy+Qnp7+EZ760zAxMUFMTMznLgblgnWj3lg/6ot1o75YN+qN9aO+1L1utLW1YWZmVqC8atVS8XZA\nAQCGhoYAgFevXiE8PBytWrWS06pUqYLSpUvj3LlzAIDg4GC4u7vL6UZGRqhVq5acHhISopSuqakJ\nNzc3OZ2IiIiIiApHrYKKd+3evRuWlpYwMDAAAFSoUEEpvVy5cnj8+DHi4uIQFxeXZzoA3Lt3L990\nIiIiIiIqHK3PXYC8XLt2DX5+fti8eTOSkpIgSZIcXGQzMDBASkoKEhMT5f1301+9egUASExMzPN8\ndZShoYM0hVTo81PjU5AJ3UKdq6MhoKVIK/S9iYiIiOjbopZBxaNHj9CmTRsMHToUHTt2xMWLFwEA\naWnKX3RTUlJgYGAAXV3dfNMBQFdXN990dZOmkLDxjioNSQKFbYjqVVmhnn8wiIiIiEgtqd13x2fP\nnqF58+Zo0aIF/Pz8AAAWFhYQQiAqKkqpC1NUVBS6desGU1NT6Orq5pjJKSoqCk5OTvI1cku3trbO\nsyyBgYEIDAxUOmZtbY2FCxeiWLFi+JRj3FPjU5AVGPz3NDU1YWJk8lnu/S3Q1taGiQnfr7pi/agv\n1o36Yt2oN9aP+lL3upGkrF4zw4YNw71795TSPD094enp+f951Wn2p1evXqFx48aoVasW1q9fr5Rm\nbW2NHj16YNq0aQCA27dvw87ODteuXYO9vT2aNm0KCwsLbNy4EUDW7E9lypTBjh070Lp1a/Tr1w93\n7tzByZMnAQAKhQJWVlbw9fXFTz/99MFl/dSzPyVBV8WWisLrVVkBA6R+lnt/C9R9podvHetHfbFu\n1BfrRr2xftSXutfNh8z+pDl58uTJn7Y4BZOQkIBmzZqhePHimDt3LuLi4hAbG4vY2FgYGRlBW1sb\nM2fOROXKlZGUlIQhQ4agZs2a8Pb2BpBVKRMnTkTJkiWhra2N4cOHQ0tLC3PnzoUkSShfvjymTJkC\nIGuWqSlTpuDWrVtYvXo1dHR0Pri8SUlJUCgUH/UdvC0dWvg7pvBjKlRRo4SANjI/y72/Bfr6+khO\nTv7cxaA8sH7UF+tGfbFu1BvrR32pe91oamrKs7G+j9p0fwoPD8fVq1cBAHZ2dgAAIQQkScL9+/fx\n888/4+XLlxgyZAhSUlLQsWNH+Pv7y+e3a9cOixYtwrRp0xAbG4tmzZrhwIEDcrNNzZo1ERgYCF9f\nX/j5+aFOnTo4duxYgV8UERERERHlTq26P31J2P2JCkvdmzq/dawf9cW6UV+sG/XG+lFf6l43X+zi\nd0RERERE9OVhUEFERERERCphUEFERERERCphUEFERERERCphUEFERERERCpRmyllib4kGRo6SFMU\nbh2R1PgUZEK30PfW0RDQUqQV+nwiIiKij41BBVEhpCkkFab8FVClkbBXZQX/4hIREZFaYfcnIiIi\nIiJSCYMKIiIiIiJSCYMKIiIiIiJSCYMKIiIiIiJSCYMKIiIiIiJSCYMKIiIiIiJSCYMKIiIiIiJS\nCYMKIiIiIiJSCYMKIiIiIiJSCYMKIiIiIiJSCYMKIiIiIiJSCYMKIiIiIiJSCYMKIiIiIiJSCYMK\nIiIiIiJSCYMKIiIiIiJSCYMKIiIiIiJSCYMKIiIiIiJSCYMKIiIiIiJSCYMKIiIiIiJSCYMKIiIi\nIiJSCYMKIiIiIiJSCYMKIiIiIiJSCYMKIiIiIiJSCYMKIiIiIiJSCYMKIiIiIiJSCYMKIiIiIiJS\nidbnLgAR0ceUoaGDNIVU6PNT41OQCd1CnaujIaClSCv0vYmIiL5UDCqI6KuSppCw8Y4qjbAChW3E\n7VVZwX9UiYjom8TuT0REREREpBK1DCpSU1M/dxGIiIiIiKiA1CaoePr0KQICAtChQweULFlSKW39\n+vXQ0NCApqYmNDQ0oKGhAVdXV6U8u3btgr29PfT19VGnTh2EhYUppYeGhsLJyQn6+vpwcHDAsWPH\nPvkzERERERF9C9QmqPDw8ICfnx9iY2ORlJSUI71s2bKIiIiQt507d8ppZ8+ehaenJwYPHowLFy7A\n0tISrVu3lq8TGRmJNm3aoGXLlrh06RIaNWqETp06ISoq6j97PiIiIiKir5XaBBUHDhxAREQE+vfv\nn2u6lpYWKlSoAGtra1hbW6NMmTJy2rx589C2bVt4e3ujWrVqWLNmDeLj47Fjxw4AwOLFi1G5cmXM\nnDkTVatWxeLFi2FiYoI1a9b8J89GRERERPQ1U5ugomzZsoU+Nzg4GO7u7vK+kZERatWqhXPnzgEA\nQkJClNI1NTXh5uYmpxMRERERUeEVOqi4fft2vulXrlwp7KVz9eDBAxQpUgS2trbw8fFBQkICACAu\nLg5xcXGoUKGCUv5y5crh8ePHAIB79+7lm05ERERERIVX6KCiT58+uR7ftGkTAOCnn34q7KVz8PDw\nQFhYGE6fPo1Ro0Zhy5Yt6NmzJwAgMTERAGBgYKB0joGBAVJSUuQ8+aUTEREREVHhFXidpocPH8qf\nJUmCEAJPnjzBo0eP5OPFixfHypUr0bNnTwghPlohzc3NYW5uDgCoUaMGjIyM0K1bNzx9+hS6ulkr\n36alKa9im5KSIgcSurq6+abnJTAwEIGBgUrHrK2tsXDhQhQrVuyjPuO7UuNTkLUI139PU1MTJkYm\nn+XeXwrWj/pi3Xy9tLW1YWLC96uOWDfqjfWjvtS9biRJAgAMGzYM9+7dU0rz9PSEp6envF/goMLB\nwQGSJMHS0hKJiYkoU6YMVq9ejT///BP//vsvypUrBxcXl4/0CPmrUaOGHNQ4OjpCV1c3x0xOUVFR\ncHJyAgBYWFjkmm5tbZ3vfd59WW9LSEhAenq6Ck+Rv0zo4nMNecnMzERMTMxnufeXgvWjvlg3Xy8T\nExO+XzXFulFvrB/1pe51o62tDTMzMyxcuPC9eQv8P2+NGjVQu3ZtrF+/Hp06dZKPL168GA4ODhgx\nYgQaNmxYuBJ/oAsXLkBTUxOVK1eGJElwcXFBUFCQnB4fH4/Lly+jefPmAIAGDRoopSsUCoSEhMjp\nRERERERUeAVuqciW3QzysT158gTJycl49uwZAODu3bsAsloZ5s+fD1tbW9ja2iIsLAyjRo3CkCFD\nULRoUQCAj48PunTpgoYNG6JevXqYMmUKqlSpAg8PDwDA0KFDUbduXUybNg2dO3fG0qVLIYSAl5fX\nJ3kWIiIiIqJvyQcHFe9KT0+HEALp6elQKBQQQuQYv1AQPXr0QGhoqLyf3QoRHBwMHR0dDB06FHFx\ncbCyssKIESMwcuRIOW+7du2waNEiTJs2DbGxsWjWrBkOHDggB0A1a9ZEYGAgfH194efnhzp16uDY\nsWMwNDRU9fGJiIiIiL55KgUVQgh4eHjg9evX+PvvvyGEQHJyMsqXL4/Y2NgPulZwcHCeaW5ubhg1\nalS+5w8ePBiDBw/OM71z587o3LnzB5WJiIiIiIjeT6XRjJIk4fjx43BxcYG/vz+WL1+OOnXqIDo6\nGo6Ojh+rjEREREREpMY+2hQpn2qsBRERERERqTeVgwpJkhhQEBERERF9wwo8puLq1auQJAktW7aE\nlpYWrK2tIYRAu3bt8OLFC9y8eZNTtBIRERERfYMKHFQkJCQo7bu6umLs2LEYNmzY/19MSwvu7u4A\n2B2KiIiIiOhbUejZn4QQ0NHRgY6OjtLxKVOmyOlERERERPT1++Cg4siRI7C2tsbp06dzTW/atCn2\n79+PvXv3qlw4IiIiIiJSfx88UHvRokUYMWIEVq9ejXPnziEjI0Mp/erVq+jatSsOHTr00QpJRERE\nRETqq1CzP6WkpOD48ePo2bMnTExM0Lt3b1y9ehXXrl2Dh4cHvLy80Ldv349dViIiIiIiUkOFCio8\nPT2xfft2REREICwsDMWLF4eTkxNcXFzg6emJ5cuXf+xyEhERERGRmnrvmIr09HQ4OjrCyckJzs7O\nePnypZx27949HDp0CH/88QccHBygUChw6dIlvHnzBoaGhp+04EREREREpB7e21KhoaGBBQsWwNbW\nFjt27MCVK1fw66+/wsTEBJ06dcKtW7ewcuVKhIeH49SpU9DS0oKHh0eOsRZERERERPR1em9Qoamp\niZYtW2Lo0KEICQnBgwcPMGDAAAghEBAQgKVLl6JZs2YAgKJFi2Lfvn2IiYnBmDFjPnnhiYiIiIjo\n8yvQmIqbN2/C2toa69atQ5kyZbBu3TqEhYXh1KlTAIDhw4cjMDBQzt+/f3/07Nnz05SYiIiIiIjU\nSoHWqahSpQoOHz4Mb29v2NjYID09HZs3b4aTkxMAIDAwEDdv3kRAQABcXFxw4cIF+Pj4fNKCExER\nERGReihQS8WaNWtw5coV9OvXDzdu3EBycjJCQ0OhpZUVk0iShEOHDqFBgwaYOXMmZs6c+UkLTURE\nRERE6qNALRVnz54FkBU8CCGQkJCA6OhoXL58GUePHkVqaioAoHz58tDT00NoaChq16796UpNRERE\nRERqo0AtFatWrcL8+fNhY2ODJUuWoGTJkihbtixatWqFtLQ0JCYmYt68eViwYAH27duH2bNnIzEx\n8VOXnYiIiIiI1ECBgop//vkHtra2OHPmjLxOxciRI+Hn54dFixbBxMQEYWFhcHR0RPPmzdG0aVPs\n3LnzkxaciIiIiIjUQ4G6Pzk4OODIkSOoUaMGAMDNzQ3NmjXDrFmzkJSUBEtLS2zZsgVpaWkAgA4d\nOshdpoiIiIiI6OtWoKACgBxQ7N69G9u2bQMABAUFAQAuXLgAANDR0QEAnDp1Cv7+/h+1oERERERE\npJ4K1P0pW2ZmJr7//nsAwJYtW5CQkJBrvt9//x0KhUL10hERERERkdr7oKAi25MnTzBixAiYm5uj\nbdu22LBhA16/fi2nCyE+WgGJiIiIiEi9FSqoKFOmDKKjo3H27Fm4uLhg2bJlKFWqFDp16oTAwEBI\nkvSxy0lERERERGqqwGMqgoODERISonTM0dERjo6OGD9+PJ48eYI9e/ZgyZIlH7uMRERERESkxgrU\nUnHkyBG4u7vLi9zlpkyZMhgyZAhOnz790QpHRERERETqr0BBRcOGDREcHIwZM2bkmv7w4UMsWLAA\nI0eOBMAxFURERERE35ICBRWGhoZwdXVVOvbs2TMsXLgQzs7OsLGxwcGDB1G5cuVPUkgiIiIiIlJf\nBR5T8bbMzExUq1YNzs7OGDp0KNq3bw8jIyM5nQO1iYiIiIi+HQUKKhQKBTQ0NCBJEnR0dKCpqYkH\nDx5AX18/1/zs/kRERERE9O0oUPenbdu2oUqVKpg7dy7u3bsHAHkGFAAQEhICDY1CzVZLRERERERf\nmAK1VLRu3RoJCQnYtGkTJk6ciJIlS+abXwgBSZLw8OHDj1JIIiIiIiJSXwUKKoyMjDBw4EAMHDgQ\nkZGRmDVrFgICAuDm5oa5c+eiRIkSn7qcRERERESkpj64j5KVlRWWL1+Oa9euwdDQEJMnT0bp0qVR\nvnx5eYuNjUVYWNinKC8REREREamZAs/+NHr0aLx8+VLpWIkSJSBJEi5dugRXV1fcuXMHvr6+OHTo\nEH7++Wd06tTpoxeYiIiIiIjUS4FbKnbs2IHq1aujUaNG8laqVClcvHgRpUqVwtixY1GjRg0UK1YM\nN27cwNy5cwtdqPxW7iYiIiIiIvXyQd2ffvjhB3h5ecmbu7s7SpcuDWtra1SvXh2XL1/G2rVrYWVl\n9cEFefr0KQICAtChQ4dcB4IvW7YM1tbWMDAwQLNmzXD//n2l9F27dsHe3h76+vqoU6dOju5XoaGh\ncHJygr6+PhwcHHDs2LEPLiMREREREeX00eZ9/e6772BnZ1fo8z08PODn54fY2FgkJSUppW3fvh3D\nhw/H9OnTcfr0aaSnp6Njx45y+tmzZ+Hp6YnBgwfjwoULsLS0ROvWreXrREZGok2bNmjZsiUuXbqE\nRo0aoVOnToiKiip0eYmIiIiIKEuBgwpJkvJdKbtLly7o1KkTrl27VqiCHDhwABEREejfv3+OtNmz\nZ+Onn35C9+7d4ejoiJUrV+LatWs4ceIEAGDevHlo27YtvL29Ua1aNaxZswbx8fHYsWMHAGDx4sWo\nXLkyZs6ciapVq2Lx4sUwMTHBmjVrClVWIiIiIiL6fwUOKoQQqFevHqytreWtW7duOHXqFHbv3o2A\ngABUrlwZDRo0QJ8+ffDkyZMPKkjZsmVzPR4fH4/w8HC0atVKPlalShWULl0a586dAwAEBwfD3d1d\nTjcyMkKtWrXk9JCQEKV0TU1NuLm5yelERERERFR4BZ79aebMmXB1dc01zdTUFDdv3sTAgQPh4+OD\n4cOHo3fv3jh+/LjKBbx//z4kSUKFChWUjpcrVw6PHz9GXFwc4uLi8kwHgHv37uWaXthWFSIiIiIi\n+n8FDir69u2LHTt2QAiRa/ratWvx5s0b/Pzzz+jWrdtHm8EpMTERAGBgYKB03MDAACkpKfmmv3r1\nSr5GXucTEREREZFqChxUZGRkYO3atRBC4ODBg2jbtq38uU2bNnjz5g3++usvFClSBEIISJKEH374\nQeUC6urqAgDS0tKUjqekpMDAwOC96dnXyC+diIiIiIgKr8BBhY6ODnbv3g0AKFq0qNLnPXv2ID09\nHSYmJpg+fTrs7e0/WgEtLCwghEBUVJRSF6aoqCh069YNpqam0NXVzTGTU1RUFJycnORr5JZubW2d\n770DAwMRGBiodMza2hoLFy5EsWLF8my1+RhS41MAfLrr50dTUxMmRiaf5d5fCtaP+mLdfL20tbVh\nYgu5KoIAACAASURBVML3q45YN+qN9aO+1L1usidpGjZsGO7du6eU5unpCU9PT3m/wEGFQqHAtWvX\nIISAECLXz/b29ggKCvqoQUWZMmVgZWWFoKAguLm5AQBu376Nx48fo1mzZpAkCS4uLggKCkLv3r0B\nZA3uvnz5Mnx9fQEADRo0QFBQECZOnCg/S0hIiJyel3df1tsSEhKQnp7+sR4zh0zo4iPO+Pth987M\nRExMzGe595eC9aO+/o+9O4+LslrcAP68s8EMiwICCo7igmtdStNc0lK7P7fMrVTctzJzCzTNNUlT\nU29X01y6aZqmuVVq5Za5lKWYa5ml4oYgKjsMszFzfn+gk8giMMAM8Hw/n/nIu593zjgzz7znnJd1\nU355e3vz+XVSrBvnxvpxXs5eN0qlEr6+vliyZMlj1y1wqDCbzRgyZAiEEAgODrZ9gX/476SkJOzf\nvx8TJkwodKFjY2Oh1+tx584dAEBUVBSArKsM4eHhmDZtGkJCQhAUFITw8HB069bNFl7CwsLQu3dv\ntGnTBi1atEBERAQaNGiAzp07AwDGjx+PZ599FnPmzEGvXr3w8ccfQwiBIUOGFLqcRERERESUXYFD\nxcSJE7Fw4cJ81zl79iw2btxYpIIMGDAAR48etU3Xq1cPQNZwsWPHjkV8fDzGjBkDg8GAHj16YNmy\nZbZ1u3XrhqVLl2LOnDlISkpChw4dsHv3btslm6eeegqbN2/GO++8g/nz56N58+bYv38/3NzcilRW\nIiIiIiL6hyRKsmNAOXbv3r0Sbf6UARdsuOyYJhyDgq3QoHhG7yqvWD/Oi3VTfjl7M4GKjHXj3Fg/\nzsvZ6+ZB86eCcMwnLxERERERlRsMFUREREREZBeGCiIiIiIisgtDBRERERER2YWhgoiIiIiI7MJQ\nQUREREREdmGoICIiIiIiuzBUEBERERGRXRgqiIiIiIjILgwVRERERERkF4YKIiIiIiKyC0MFERER\nERHZhaGCiIiIiIjswlBBRERERER2YaggIiIiIiK7MFQQEREREZFdGCqIiIiIiMguDBVERERERGQX\nhgoiIiIiIrILQwUREREREdmFoYKIiIiIiOzCUEFERERERHZhqCAiIiIiIrswVBARERERkV0YKoiI\niIiIyC4MFUREREREZBeGCiIiIiIisgtDBRERERER2YWhgoiIiIiI7MJQQUREREREdmGoICIiIiIi\nuzBUEBERERGRXRgqiIiIiIjILgwVRERERERkF4WjC0BERBVDpkwFk1Uq8vbGFAMscCnStiqZgMJq\nKvKxiYgofwwVRERUKkxWCRsu23OBXKCoF9gHBVv5gUdEVILKTPOniIgIyGQy20Mul6N///625StX\nrkTt2rWh0WjQoUMHXLt2Ldv2O3bsQKNGjaBWq9G8eXOcPn26tE+BiIiIiKhcKjOhAgCeffZZREVF\n4cqVK7h8+TKWLl0KANi6dSvCw8Mxd+5cHDt2DGazGT169LBt9+uvvyI0NBSjR49GZGQktFotunTp\ngoyMDEedChERERFRuVGmrgar1WrUqlUrx/wPPvgAb775pu3KxSeffIJGjRrhyJEjeP7557F48WK8\n9NJLGDduHABg7dq1qFq1KrZt24YhQ4aU6jkQEREREZU3ZepKRW5SUlJw5swZdOzY0TavQYMGqFat\nGo4fPw4AOHToEDp16mRbXqlSJTRp0sS2nIiIiIiIiq5MhYqjR4/C3d0dTzzxBN577z2YTCZcu3YN\nkiTluIJRo0YNxMTEIDk5GcnJyXkuJyIiIiIi+5SZ5k/Dhw9Hr169YDabceTIEcyaNQvx8fHo06cP\nAECj0WRbX6PRwGAwID09Pc/lCQkJpVN4IiIiIqJyrMyECq1WC61WCwBo0qQJMjMzMXv2bAwaNAhC\nCJhM2ccfNxgM0Gg0cHHJGtM8r+VERERERGSfMhMqHhUSEgKDwYCqVasCAKKjo7M1cYqOjka/fv1Q\npUoVuLi4IDo6Otv20dHReOaZZ/I9xubNm7F58+Zs82rXro0lS5bA09MTQohiOpucjCkGZI3JXvrk\ncjm8K3k75NhlBevHebFunBfrpvxSKpXw9ubz66xYP87L2etGkrJuWPrWW2/h6tWr2ZaFhoYiNDTU\nNl1mQ0VkZCR8fHyg1WoRFBSEAwcOoG3btgCAS5cuISYmBh06dIAkSWjZsiUOHDiAwYMHA8jq3H3q\n1Cm88847+R7j0SfrYampqTCbzcV7Ug/JumusY7q8WCwWJCYmOuTYZQXrx3mxbpwX66b88vb25vPr\nxFg/zsvZ60apVMLX1xdLlix57LplJlS8/fbbeOGFF6DVanH48GEsWLAAc+fOBQCEh4dj2rRpCAkJ\nQVBQEMLDw9GtWzc0atQIABAWFobevXujTZs2aNGiBSIiItCgQQN07tzZkadERERERFQulJlQYTab\nMXz4cOh0OtStWxdLly7FiBEjAABjx45FfHw8xowZA4PBgB49emDZsmW2bbt164alS5dizpw5SEpK\nQocOHbB7927bJR0iIiIiIio6SZRkx4By7N69eyXa/CkDLthw2THNBAYFW6GB0SHHLitYP86LdeO8\nWDfll7M34ajoWD/Oy9nr5kHzp4IoU/epICIiIiIi58NQQUREREREdmGoICIiIiIiuzBUEBERERGR\nXRgqiIiIiIjILgwVRERERERkF4YKIiIiIiKyC0MFERERERHZhaGCiIiIiIjswlBBRERERER2Yagg\nIiIiIiK7MFQQEREREZFdGCqIiIiIiMguDBVERERERGQXhgoiIiIiIrILQwUREREREdmFoYKIiIiI\niOzCUEFERERERHZhqCAiIiIiIrswVBARERERkV0YKoiIiIiIyC4MFUREREREZBeGCiIiIiIisgtD\nBRERERER2UXh6AIQERGR42XKVDBZpSJta0wxwAKXIh9bJRNQWE1F3p6IHI+hgoiIiGCySthwuagN\nGATsafwwKNjKLyREZRybPxERERERkV0YKoiIiIiIyC4MFUREREREZBeGCiIiIiIisgtDBRERERER\n2YWhgoiIiIiI7MJQQUREREREdmGoICIiIiIiuzBUEBERERGRXRgqiIiIiIjILhUuVERERCAwMBDu\n7u7o3bs3EhISHF0kIiIiIqIyTeHoApSmhQsXYvny5Vi/fj28vb0xfPhwDB06FLt373Z00YiIiIhy\nlSlTwWSViry9McUAC1yKtK1KJqCwmop8bKo4KkyoEEJg8eLFmDVrFrp06QIA+PDDD9G1a1fcuHED\nNWvWdHAJiYiIiHIyWSVsuGxP4xKBojZOGRRsrThfFskuFeZ18vvvvyMhIQEdO3a0zXvhhRcgSRKO\nHz/OUEFEREREhcKrSP+oMKHi6tWrAIBatWrZ5rm6usLX1xcxMTGOKhYRERERlVG8ivSPCtNROz09\nHTKZDEqlMtt8jUYDg8HgoFIREREREZV9zhRwSpSLiwusViusVitksn+ylMFggEajKfT+FIqSfepc\nIEc1d8dkPhelBCWUj1+xAmP9OC/WjfNi3Tg31o/zYt04r/JeN4X5visJIUQJlsVp/PLLL2jTpg2u\nXbuGGjVqAABMJhPc3d2xfft2vPzyyzm22bx5MzZv3pxtXtu2bTFp0qRSKTMRERERkaMtXrwYR48e\nzTYvNDQUoaGhtukKEyoMBgN8fHzw0UcfYcSIEQCA/fv3o1u3brhz5w4qV67s4BIWr7feegtLlixx\ndDEoF6wb58b6cV6sG+fFunFurB/nVZ7qpsI0f3J1dcXo0aMxa9YsaLVauLm5ISwsDKNHjy53gQL4\np2M6OR/WjXNj/Tgv1o3zYt04N9aP8ypPdVNhQgUAzJs3DwaDAX379oVcLsegQYOwcOFCRxeLiIiI\niKhMq1ChQqVSYfny5Vi+fLmji0JEREREVG5UmCFliYiIiIioZMhnz54929GFoJLx5JNPOroIlAfW\njXNj/Tgv1o3zYt04N9aP8yovdVNhRn8iIiIiIqKSweZPRERERERkF4YKIiIiIiKyC0MFERERERHZ\nhaGiHBJCwGg02h5ms9nRRaICWr9+PSIjIx1djHLBaDQW6/5YN6XLYrHAarUWadudO3di//79xVwi\notKR13vXsmXL8Pfffxd4P3fv3oVOpwMApKWl4ZNPPsmxzrlz5wq1z4qmqJ8jhf28SExMREpKCgDA\nbDZjxYoVOd7/Ll++jDNnzuS7nx07dkCr1Ra+wMWEocLJGY1GpKSkFOrxww8/wMPDA1qtFv7+/mjR\nogXWr18PFxcXeHt7w8vLy/avXC7HzZs3HX2aZcrWrVvx1FNPwc3NDTVr1sT777//2G3Wr18PmUwG\nuVwOmUwGmUyGVq1aZVvn9u3bGD16NEOgHeLi4rBmzRp0794d/v7+Bdrmxx9/RIsWLeDp6YmAgACE\nhYXlqAPWTeFFRETYXusPXvv9+/fPd5vk5GRs2rQJ/fv3h6+vL+Li4nKss2PHDjRq1AhqtRrNmzfH\n6dOnsy03GAwYPXo07t27V6znQ/84deoUPv7442LbX4cOHTBhwoRi219Z9Lj3rjNnzmDixIlQq9UF\n3ueAAQOwY8cO2/TMmTOxa9eubOssX74cW7ZssU3Hx8cjJiamUI+MjIwinLHzelxdrFy5ErVr14ZG\no0GHDh1w7dq1bMuL8nkxZcoULF26FAAgl8uxYsWKHPdU27p1K5YtW/bYfUmSVODjFjeGCic3d+5c\nWwh4+OHl5WV7PDzP29sb165dQ4sWLXD37l3MmTPHNlTZgAEDkJiYiKSkJNu/NWvWdPAZlj1///03\npk2bhhMnTmDGjBmYPXt2rr8APap69eq4cuWK7bF9+/Zsy+fPnw+z2Yx27dpBpVJBqVRCLpdDLpdD\npVLZ5qlUKnzzzTcldXplWufOnTF//nwkJSUV+IPu4sWLGDVqFI4dO4YlS5bgs88+w7vvvpttHdZN\n0Tz77LOIiorClStXcPnyZduHZl6GDRuGyZMnIyEhwfar3cN+/fVXhIaGYvTo0YiMjIRWq0WXLl2y\n1fWKFStw9+5dDBs2zFYvCoUi17pasmRJsZ9zWVKYH61MJpNtu1mzZtnq54033rA9nw8eCoUCfn5+\ntvVbt25t+4xSKpX46aefbMuOHz+OM2fOYNu2bUhLSyu9k3cyj3vvevfddyGEQN26dbO9BykUihyv\n69x+zfbw8EBERESuP5o8rGfPnqhRo0a2h1arhVarzTH/wWPjxo3F+lw4Wn51sXXrVoSHh2Pu3Lk4\nduwYzGYzevTokW0dez8vZDIZFi9ejHfffReJiYkleq7FTlCZtHbtWuHp6Slu3LiRY9nhw4dFmzZt\nhBBCdO3aVWzfvl2sW7dODB06NMe6QUFBue6DCq5r166id+/e+a6zbt06UatWrTyX7927V3h5eYnL\nly9nmz9z5kwxbty4YilnRRAdHS2EyHq+lUplkfYxZswY0bRpU9s066ZoZs+eLdq1a1eobWJiYoQQ\nWe9hMpnMNv1Ar169RM+ePW3TycnJwtXVVaxbt04IIcS5c+eEp6en+Pnnn7Ntt2bNGtGtW7einEa5\n9s477whJkoRMJrM9JEnKNu/B3x988IEQQojffvtNaLVaodfrc92n0WgUzz//vJg+fXquy5s3by7+\n+OMPIYQQFotFPPPMM2LVqlVixowZYuTIkSVzomVAfu9dn3zyidBqtSIhISHb/EGDBokPP/wwz32+\n+OKLYv369bZps9ks6tSpIz766CPbvGHDhon3338/37ItX75cPP300wU+l7Iuv7po0qSJCA8Pt01f\nvHhRSJIkDh8+LIQo+ufFyJEjRURERLZ5bdu2zXasiIgI8dprr+Vb9u3btwutVpvvOiWJVyrKoKio\nKEyYMAHLli1DjRo18lzPaDTi119/RceOHQEAX375Jfz8/ODr62v799atW6VV7HLLarXCx8enyNtH\nR0dj4MCBWL16NerWrZttmeBtZAqlevXqdu/j4fpk3ZSugICAfJcfOnQInTp1sk1XqlQJTZo0wfHj\nx5GamorevXtjxowZaN26dbbtito3o7ybP38+rFYrLBaL7eHh4YFz587Zph8snzx5MgAgLCwMCxcu\ntDXxeJher0ffvn3h5+eHOXPm2ObfuXMHr732GgAgISEB3t7eALKayLm7u2PUqFGYNWsWIiMjsWHD\nhlI6e+eS13vX6dOnMXHiRHz55Ze25+0BIUShXtsKhQJff/01Ro4caZun1+sL1aSqIsirLlJSUnDm\nzBnbdyoAaNCgAapVq4bjx48X++fFp59+ihkzZtimC1pXVqsVd+7cyfEojWZqihI/AhWr1NRU9OzZ\nE66urtBqtbBYLJDL5bmue+TIETRr1gzu7u4AgNDQUKxduzbbOrVr1y7xMpdXGRkZ2Lx5M06cOIFF\nixY9dv0bN27A3d0dgYGB6NKlCyIiIuDp6QkfHx8sXLgQGo0GHh4e2dpDGo1GSJKEdevWAch6Y5Ik\nCZcuXULVqlVL6tQqJJPJhL1792LLli3YunUrALBu7HT06FG4u7sjKCgIffr0wTvvvAOVSlWkfSUn\nJyM5ORm1atXKNr9GjRqIiYmBu7s7Jk2ahJYtW8Ld3R0y2T+/mZnNZlgsFnh6egL4p64OHDiAZ599\ntugnWM4YjUakpaXl+fpdtWoVXF1d0a9fP/Tu3RtBQUG2ZdeuXUPfvn1x7tw5fP3119n+r9y8eRPX\nr18HkNUh1dvbG1988QXWrVuH48ePAwCUSiW2bNmCDh06FKj/TUWh1Wrx6aef4ty5c+jYsWO259Vg\nMGD79u2IiIgAkPW6ViqV+TaZefTOzffu3UOVKlUeWw69Xo8ePXrgjTfeyBbsK5Jr165BkqQ834Oq\nVKlSrJ8XwcHB2abv3btXoCbrsbGxuf5AM3/+fNuPAyWFVyrKEKPRiO7du6N+/fpo1qwZIiMjMWHC\nBPTs2RO3b9/OsX5qaiqSkpJsCTm3pMxfW4tGrVbD3d0dEydOxPLly9G4ceN81+/cuTNOnz6NY8eO\n4e2338amTZswcOBAAIBGo8GwYcOQmZmJZs2aITU11faYPHkyXn/9ddt0WloaUlNT+aW1mDVo0ACu\nrq7o06cPZsyYgQ4dOgBg3dhj+PDhOHPmDI4ePYoRI0Zg0aJFmDRpUpH3l56eDiCrTh6m0WhgMBgg\nk8kwatQoCCHg5+eXra6WL1+OTp065airihwoDAZDjn4Tp0+fhoeHB1QqVY5lycnJ2LZtGyIjI+Hr\n64vTp09jzpw5SEpKQkREBJ555hkMGDAA33zzDcaNG4dOnTrhr7/+ApDV8dXPzw9CCJhMJuzbtw+T\nJk3CjBkz8Oeff+LgwYM4ePAgYmJiMH/+fISHh+PIkSMOfoacg6+vL/r06QOTyYRXX3012+u6b9++\neP/997O9rgvbBv/KlSvZwmFeFAoFmjRpgh49euCVV15BTExMEc+o7Hrce5BarS7Rz4uC1lX16tWz\nXX188CjpQAEwVJQZCQkJaN++PTQaDTZv3gyZTAZJkjBr1ixIkoTGjRvj008/zbbNK6+8gsqVK9vm\nf/nll/Dx8YFKpYKfnx/8/Pzg6enJpgFFcO7cOZw4cQLz58/H+PHjbZcog4ODoVQqbZ2woqOjAQB+\nfn4ICQlBSEgIRo4cieXLl+O7777LdXQbKhl51Q0A7NmzB7/99htWr16NZcuWYcCAAQ4safmg1Wrx\n5JNPokmTJggLC8OsWbOwZs0aAPnXRV5cXFwAIFuHYSDry/GjH/L0eFOnTs0xCEjr1q2Rnp6eY2AQ\nb29v+Pj4oGnTprhy5Qrc3d2xcuVKfPXVV9BqtTh16hR++uknTJgwAZ07d8aff/5pq/u9e/ciLi4O\nvr6+SExMROXKldGqVSscOnQIFy5cwOrVq7Fq1SqsXr0aq1evxrVr1/Dbb7/h+eefd/RTVO7dvHkT\nMTExePrppx+7rlKpxKxZs3Du3DnExcWhUaNGWL16dSmU0nk48j3IaDTi5MmTaNq0aYkex14MFWXA\n+fPn0bp1a1SqVAk7duyAQvFPqzU/Pz989dVX+PjjjzFp0iS0b98eV65csS0fOXKkbZSh0NBQxMbG\nolq1ajh37hzu3r2Ld955B+3atct1pBXKW7169dCsWTOMHj0aixYtwsKFC2E0GrF//35cuHABFy5c\nwB9//JFnG/GQkBAIIRAbG2ubJ4TglaMSlF/d1KpVC02aNMGQIUOwdu1abN68Odv/I9aN/UJCQmAw\nGJCYmFjg/ycPq1KlClxcXHIEkOjo6GzNOHOrK9ZdTv/9739z9Kd4+eWXMW/evFx/5bRYLFi4cCHG\njBmDzp07o1OnTujUqROOHDmCXbt2oVGjRrZ9u7i4YNGiRTh48CDat2+PlJQUfP755wgODkZqaipG\njRqF+vXrY/bs2Vi9ejW8vLywdetWbN26FUFBQRy6ORfF8R60Z8+ebO9rX3zxBVq3bm1rIl0Q9evX\nx08//YS3334b48ePt/1QUBEEBgZCCFGk96DCOnHiBE6cOGGb/uqrr+Dv75/t/5lTKqUO4VRES5Ys\nEWq1WsyePTvb/Jdeesk2GscD169fF61atRLr16+3jf70448/ipCQELF+/XoxbNgwIYQQ06ZNE23b\nthV9+vQR//rXv8Tvv/9eOidTTn3++edCoVAInU5X4G02bNggFAqFSE1NFe+9955QKBRCoVAImUwm\nlEql7SGXy4VcLs82T6FQCF9f3xI8o7LPntGfjh49KmQymfjrr79YN8Vozpw5BX5u8hr9qV27dmLg\nwIG26eTkZKHRaMS3335r+3+oVCpz1JVCoci1rpRKpUhNTS3W8yyr4uLihFKpFJcuXRIrV64Uu3bt\nyrHO4sWLhVqtFvPmzRNvvvmm+OCDD8SUKVOyPe9yudw2/fXXX+fYx7fffiv69+8v1q1bZxs1z8/P\nT1y7dk2kpqYKDw8Pce3atZI+Xaf16HvXiBEjbO9Bj76GH36uH35dN2vWTAjxz+hPt2/fFn369BEe\nHh7i/PnzQois+vb19RVNmjQR6enp+ZYpr9Gfzpw5IzIzM4vx7J1Lbp8jtWrVEjNmzLBN//3330Im\nk4k//vjDrs+LB6M/paSkiDfffFOo1Wqxd+9eIYQQer1e1K9fX9SsWVPcvn073zI7evQndtR2cn5+\nfvj666+zjTaQl5o1a+Lnn3/G0aNHodPp8Oqrr+LChQto27ZtttQ8Y8YMNG3aFDdu3MAff/xRqF8p\nKrq0tDSMHTsWAwcORLVq1XD27FlMmTIF/fv3z/fy5/vvv4/69eujfv36OH36NN5++22MGTMGHh4e\nmDlzJmbOnJnrdjNnzkRKSgo++uijkjqlciU2NhZ6vR537twBkDVSGpD1C5Orq2uu2wwePBh9+/ZF\nUFAQLl26hKlTp6JNmzaoX78+68YOb7/9Nl544QVotVocPnwYCxYswNy5c/Pd5t69e0hNTcWtW7cg\nhMC1a9eg1+vh7+8Pd3d3hIWFoXfv3mjTpg1atGiBiIgINGjQAF26dIEkSRg0aFCu+12zZg127tyZ\n48Zf9I958+ahVatWCA4Oxrx589CwYUN069Yt2zqurq7o3bs3hBB4/vnn0bx5cwQFBWHBggUAgNde\new0NGzZEeHi4bRuDwYCdO3fizJkzOHnypO3GhS+//DLGjh2LlJQUdOjQAXv27IFarUaLFi0K1G68\nvMnrvevjjz/O0bT5gUGDBuHpp5/O9nw/LCMjA1u3bsX48ePx3HPP4c8//0T16tWh0+nQr18/NG3a\nFCqVCt27d8eePXugVCoLVeannnqqUOuXFfl9joSHh2PatGkICQlBUFAQwsPD0a1bNzRu3BiNGzcu\n8udFRkYGDh8+jFWrVqFmzZo4efIkGjduDIvFgqFDh0KtVuOFF17A//3f/+Gnn35CpUqVSubk7cRQ\n4eRCQ0MLtf6D0Qbc3NzQrVs39OnTB927d8fmzZshhMDPP/+M6dOnY/v27ejatSs6deqEtWvXol69\neiVR/HLH1dUVZrMZQ4YMQUpKCmrWrIkJEybk+ab+gEqlwoQJE5CUlISgoCBMnDjRrk6rlLsBAwbg\n6NGjtukHr+tDhw6hbdu2uW5TuXJljB49GvHx8QgMDMQrr7yCadOmlUp5yzOz2Yzhw4dDp9Ohbt26\nWLp0KUaMGJHvNpMnT8b69eshSRIkSbLV2WeffYbBgwejW7duWLp0qa2DcIcOHbB7926H3kG2PDh+\n/DhWrVqFyMjIPNf54osv4O/vbxvy9c6dO/jjjz8KFAAOHz6MFi1aYNiwYYiMjERkZCS8vLzQpk0b\n7Nq1C6Ghobhz5w7WrVuHsWPHFtdplSlFee/Kz+XLl3Hq1ClUqlQJq1atQr9+/QAAly5dsvUZ++ab\nb6BQKNC6dWuEhoZi27Zt/L+E/Oti7NixiI+Px5gxY2AwGNCjR48C3eU6P0lJSdi3bx9MJhPef/99\njBs3DkDWnbmHDh2Ky5cv4/DhwwgMDETXrl3RpUsX/PDDDzCbzdDr9Tn29WBI2dy4u7vDzc3NrvLm\ny2HXSMguuTV/euDhm989MGXKFKHRaESNGjXE559/LoQQ4vbt26Jjx45CqVTaLrORc5kxYwZvsOak\nWDdlx6effsqb3+XhwoULwt/fX8yaNcs2b+jQoTk+X9566y3h5eUlevbsKerUqSOaNm0qZs6cmW2d\nkSNHisWLF+d7vJUrV4rJkycLIbKa4DxoPnP16lXh5+cnzGZzcZxWhTBw4EDxn//8J9dlJpNJjBkz\nRsTHxwshhEhJSRHTp08Xbm5uol+/ftma/d28eVNUq1YtzxvpVbSb35WEx31ehIeHi6tXrwohhDAY\nDGLx4sXCx8dHvPjii9maPKWkpIjGjRuL8ePHi4EDB+a4eeXjHlOnTi3R8+SVijKqsL8mNGnSBOPG\njUNERIRtBIOqVati7969OHr0aJF+CSEiorLrwIEDGDx4MF555RXbvQ4AwNPTExcuXLBN63Q6XHsd\neQAAIABJREFUHDt2DHPmzEH9+vXRqlUraDQaLFq0CEql0vZ5ZLVaIUkSpk6dahuH/+DBg2jWrBli\nYmJQs2ZN/PLLL7BarbZx/MX9prlWqxVmsxleXl6QJMnWVIfylt/3AKVSieXLl9umdTodTp06hW3b\ntqFz587Z1tVqtTh06FCO+y9Q6fnPf/5j+zszMxNHjhzB0qVLc4xE6Onpie+//x6enp6oXLmy090s\nUhKCw2IQERFVJEuWLMHUqVMxd+5cTJw4Mduy8+fPo2fPnoiJiYFMJoMQAiEhIdi9ezd8fX0LfayM\njAxUrlwZVqsVTz75JPbv31+k/RCRc2OoICIiqmBiYmIQHx+PkJAQRxeFiMoJhgoiIiIiIrILb35H\nRERERER2YaggIiIiIiK7MFQQEREREZFdGCqIiIiIiMguDBVERERERGQXhgoiIiIiIrILQwURETlE\nSkqK7e9bt25Br9c/dpt9+/Zh69atxXJ8IQTq16+PJUuWFMv+iIgqMoYKIiIqsoSEBNy5c6dAj4dD\nxK+//ooaNWrAaDQCAF544QV8++23jz3epEmTYLFYAACdOnWCUqmESqWyPRQKBZo3b25bX6vVwtvb\nG15eXpDL5YiOjrYt27JlC5KTk/G///0PVqu1uJ4SIqIKiTe/IyKiIgsODsbVq1ezzXvwsSJJUrb5\n3bt3x1dffQUAeO2116DX67Fx40YcPHgQvXr1wt27d+Hi4pLnsXbs2IEFCxbg5MmTuS5PSkrCc889\nh7CwMIwcOTLHcn9/f1y9ehVubm5IT09Ho0aN8L///Q8bN25EgwYNMH369EKdOxER/YOhgoiIilXP\nnj3x9NNPY9asWbku1+l0qFatGnbs2IF///vf6NWrF7777jvb1QKlUgkgK5xIkoTZs2dj/PjxCAkJ\nwbp16yCEwP79+zF79mzbPhMTE/HSSy+hVatWWLx4sW3+uXPnsH37dsyZMwdubm7Q6XQAgKFDhwIA\n1q1bh6SkJDz11FP47LPP0L59+xJ4RoiIyj+FowtAREQVy6pVq6DT6dC6dWvs27cPP/74I65evYpP\nP/0UKSkp+PDDD3Ns884776Bt27Zo2bIlmjdvjsGDB9uWnT17Fq+++ioSEhKwdOnSbNtdunQJcXFx\nSE1NhaenJwBg3rx5OHv2LI4dOwYA8PLywsaNG/Hqq69i06ZNDBZEREXAUEFERKXmzp07mDNnDgAg\nIyMDb775JubNm4fAwEAA/zSdetS2bduQnJyMHTt2IDg4GGPGjMHt27exYMECbNq0CcuWLUNmZia6\ndeuG9u3bY9GiRQgMDERcXBz8/PyQmJgIb29vLFu2DJ999hneffddHD9+PNsxZs6ciV69euHUqVOo\nU6dOyT4RRETlDDtqExFRqZkyZQpefPFFAFnNnN588028+eabtuUrVqyAp6cnPD094eHhgYCAAABZ\nzZhOnjwJhUKBTz/9FAsXLkTt2rWRlJSEM2fOoF+/fhg4cCD+/vtvKBQKNGzYEOfPn0dcXByqVKmC\nhIQEeHl5oXPnzvjpp5+wZ88erF69GqtWrcLq1auxevVqyGQy/P777wwURERFwFBBRESlRqfTYcWK\nFRBCQKlUYuLEidmWjxkzBqmpqUhNTUVaWhpiY2MBAK6urhgyZAgmTZqEJ598Eq+++irOnz+Pzz//\nHNWrV7dtX6lSJXz++efYtWsX/vWvfyEtLQ1z5szBv//9b5w9exYrV66Ej48PVq9ejXnz5qFu3brY\nunUrtm7dCqvVCrVaXarPBxFRecFQQUREpWbr1q3w8fHJMTLUA3k1fwoLC8Pff/8NAHj99ddx9OhR\nzJw50zakrFwuh0KhsE1XqlQJAPDRRx8hMTERiYmJ+OCDD+Dq6op58+Zh9uzZ8PX1xfLly2EwGBAV\nFYVp06ZBo9GUzIkTEZVzDBVERFRq8goTDyxfvhwajQYajQZqtRoajQbR0dHw9vbGSy+9BEmS0Llz\nZ3Tq1AlffvklzGYzTCYTOnTogG3bttmmn376aSQnJ2PDhg0IDw9HmzZtcOHCBajVavTu3RubNm2C\np6cn/vWvf+HQoUPYsGED+vbty1BBRFRE7KhNREROY9y4cbmO/hQREWH7++bNm7hy5QqqVq1qmyeE\nyHGVIy0tDb/99htatmyJ8ePH44svvoCrqyueeOIJVK5cGT///DOGDRsGo9GIzz//vNju1E1EVBEx\nVBARUZHodDoYDIZs84QQMJlM0Ol0SEhIyLGNm5ub7T4UhTF//nwcO3YMFy5cQEBAAEJDQ/Hcc8/l\nOPbDtFpttiFm09LS4O3tDQA4ePAgqlWrhjZt2uDw4cPw9PTEM888U+hyERFRFoYKIiIqkrFjx2L9\n+vW5Nmnau3dvtpvQPbBo0SJMmDAhz74Tealduzaee+45tGjRAkqlEmPHjoVSqbQd22q14vDhw5Ak\nyXbTvKioKHh4eCA9PR2+vr44ceIEYmNjMXnyZNt2QghkZmbCarXCw8MDkiQhNTW1CM8GEVHFxjtq\nExFRqbJYLFCpVEhLS8vWhyEiIiLPm98V1V9//YUnnngCkiThueeew759+6BSqYpt/0RElIWhgoiI\niIiI7MLRn4iIiIiIyC4MFUREVO4ZjcZi3d/69esRGRlZrPskIirLGCqIiKhciouLw5o1a9C9e3f4\n+/sXaJsff/wRLVq0gKenJwICAhAWFgaz2Zxtndu3b2P06NE55hMRVWQc/YmIiMqlzp07Iy0tDQEB\nAcjIyCjQNhcvXsSoUaPwzDPP4OLFi3j99dehVqsxb9482zrz58+H2WxGu3btAGSNIGW1WgEAcrnc\nNk+SJGzduhU9evQo5jMjInI+7KhNRETl0q1bt1C9enWsX78er732GkwmU6H3MXbsWBw/fhy//fYb\nAGDfvn0IDQ1FZGQk6tata1tv1qxZSE5OxkcffVRs5SciKkvY/ImIiMql6tWr270Pq9UKHx8fAEB0\ndDQGDhyI1atXZwsUQM4b7xERVTRs/kRERPQIk8mEvXv3YsuWLdi6dSsAwMfHBwsXLoRGo7HdKO8B\no9EISZKwbt06AP80f7p06RKqVq3qiFMgIipVDBVEREQPadCgAS5dugSVSoX58+ejQ4cOAACNRoNh\nw4Zh586daNasGX788UfbNjNnzkRKSgqbPxFRhcXmT0REVOEEBwdDqVRCqVRCpVIhOjratmzPnj34\n7bffsHr1aixbtgwDBgxwYEmJiMoGXqkgIqIKZ//+/dmGhA0ICLD9XatWLdSqVQtNmjRBzZo10b59\ne0RERNj6UQgh2IeCiOgRDBVERFTh1KpVq0DryeVySJIEi8WCOXPm4L333gOQ1YFbpVLZ1nswpOyq\nVats84QQ8PLywt27d4ux5EREzolDyhIRUbkUGxsLvV6PHTt2YMaMGbh48SIAIDAwEK6urrluM3jw\nYPTt2xdBQUG4dOkSpk6diqpVq+Lw4cP5Hot9KoioouOVCiIiKpcGDBiAo0eP2qbr1asHADh06BDa\ntm2b6zaVK1fG6NGjER8fj8DAQLzyyiuYNm1aqZSXiKgs45UKIiIiO/FKBRFVdBz9iYiIiIiI7MIr\nFUREREREZBdeqSAiIiIiIrswVBARERERkV0YKoiIiIiIyC4cUpaIiEqVEAJWqxVWqxXCYoE1PR1W\nvR7WzEzAbM56PNzdL6+/AUAuBxQKQKGAJJdDUiggc3WFzN0dklwOmUwGmUwGSZJK5+SIiCoodtQm\nIqJiJ4SAEAKZZjMsCQmwZGQAJhNgNAI6HWR37kAWEwNZdDSku3chi4+HLCEBktFYtOPJZBDu7hCV\nKsHq5QXh5wdrzZqwVq8Oq78/oNEAbm6QqdVQ+PhArlQybBARFSOGCiIispvVaoXFYkFmQgIsOh2E\nTgfZrVtQnDwJ+e+/Q379OiSLxaFlFHI5LDVrwtKwISxNm8IaFAS4u0Pm6gq5tzfkLi6Qy+UMGkRE\nRcBQQUREhWaxWGBOTUVmUlJWgIiLg/zUKSjOnYM8KgqS2ezoIhaIkCRYAwJgadAAmU2bwhocDFSu\nDIWXF5SVK/NqBhFRATFUEBHRYwkhkJmZCfPdu7CkpUF++TKUe/ZAceYMJIPB0cUrVkKpRGZICMxd\nu8JSvz7kHh5Q+vtDoVAwYBAR5YGhgoiIciWEgNlohDkuDtbkZCiPHYPyhx8gu34dFeWrtQBgDQqC\n+cUXkdmqFVC5MpS+vlBqNJDJOIAiEdEDDBVERGRjCxK3b0MkJUFx8CBU+/dDFh/v6KI5BaunJ8yt\nW8PcsSMQGAhV1apQurnxCgYRVXgMFUREBKvVClNiIszx8VAeOADVN99Alpjo6GI5NaFWw9S1K0wv\nvwx5lSpw8fODXC53dLGIiByCoYKIqALLzMyEMTYW4vZtqDZuhPKXXyBZrY4uVpkiAFgaNoRx6FCI\nOnWgCgiA0tWVVy+IqEJhqCAiqmCEEDDrdDDGxUF+7hxc1q+HPDra0cUqF6weHjD17Alzx45QVKkC\nlypV2PeCiCoEhgoiogrCarXCeO8eMuPjodq1C6rduyHp9Y4uVrkkAGQ2aQLj4MGQataEq1bLplFE\nVK4xVBARlXNCCBju3YPl5k24rFiRNQysowtVgVgCAmAYPx5o2BCugYEMF0RULjFUEBGVU0IIGJOT\nYY6JgeuqVVAcO8Yw4UAWrRaGceOA+vUZLoio3GGoICIqZ4QQMGdkwHjrFlSbN2c1c2Lna6dhqVkT\n+vBwyIKD4ervzz4XRFQuMFQQEZUjZqMRhuhoKPfuhcuGDZBMJkcXifJgDgmBYcIEKLVauPj4cLQo\nIirTGCqIiMoBi8UC/c2bkJ04AdeVKyFLTXV0kagABADziy/COGIEXGvUgFKjcXSRiIiKhKGCiKgM\ns3XC/vNPqBcsgPzOHUcXiYpAuLhAHxYG0bo11AEBbBJFRGUOQwURURllsViQcf06XNasgXLPHnbC\nLgcyn3wS+ilT4BIUBJWbm6OLQ0RUYAwVRERljBACxoQEZP71FzSzZ0OWkODoIlExEioV9OPHQ7Rt\nC3VgIK9aEFGZwFBBRFSGWCwWZNy4AdWGDVB98w2vTpRjmY0aQT9tGlxq1oTK3d3RxSEiyhdDBRFR\nGSCEgDEpCZmXLkHz7ruQ3b3r6CJRKRBKJQxjxsDarh3U1avzqgUROS2GCiIiJ/dgZCflli1QbdnC\nqxMVUGa9etDPmgXXOnWgdHV1dHGIiHJgqCAicmLmjAwYLl2CZvp0yGNjHV0cciDh6grdvHlQPvUU\nXLy8HF0cIqJsGCqIiJyQEALG+HhYTp6EJiKCN7EjAICQJBjeeAPWrl2hCQjgDfOIyGkwVBARORkh\nBDJu3YJ827asu2I7ukDkdEzt2sE4bhzcatZkPwsicgoMFURETkYXGwvlf/8L1Q8/OLoo5MQsQUHI\nmDcP6rp1oVAqHV0cIqrg+PMGEZGTcfX3h7F/fwiVytFFIScmv34dbqNGwfDbbzClpjq6OERUwTFU\nEBE5GblcDtf69ZExdy54KZnyI0tLg9vYscj8/ntkxMWBjQ+IyFEYKoiInJBSo4G8aVMYRo1ydFHI\nyUlWKzQffAD5J58gIyaGwYKIHIKhgojISbl4eUF06wZThw6OLgqVAS5ffw3FqlUMFkTkEAwVRESl\nwGDOQFzqNVgslgJvI0kS1AEBMI0di8zg4BIsHZUXLt99B+XKlQwWRFTqGCqIiEpYqjEef5n2YJPb\nC4jNuAir1VrgbSVJgqZGDejnzIGVNzyjAlB9/z2Uy5cj49YtBgsiKjUMFUREJUQIgYSMWzhtXYNv\nNK8gVX4Tu9WDcDfjaqG+7MlkMmjq1oVu8WIIDh1KBaDatw/KZcsYLIio1DBUEBGVACEE4jOi8bN8\nHg6r38GDO9jdVZzFYdV0JOijC7U/uVwO1wYNkPHeexwRigpEdeAAlEuXQsdgQUSlgKGCiKiYCSFw\nL+MGjihm4YzLyhzL/1JtxXlpI1IM8YXar1KjgbxZMxhef724ikrlnOrgQaj++18GCyIqcQwVRETF\nSAiBu7rr+FE5GX+4rM9zvaOu03HdchQGs75Q+3fx9oa1e3eY2rWzt6hUQagOHYLqP/+BLjqawYKI\nSgxDBRFRMRFC4I7uKva7jMdfqm35rywBuzQDEGM4W+gRoTTVqsE0fjwy69Sxs8RUUaiOHIHyf/+D\nPi7O0UUhonKKoYKIqBhkBYoo7HF5A1HKbwu0jUUy4Cu3Xrhd1BGh3n+/TI8IddloRP/r11HjwgVU\nPn8enaOicMVoBACsT0yE7OxZyM+ehez+o9WlS3nu6w+9Hs9fvgzNuXOo8+ef+CIx0bZMZ7Gg//Xr\nqHz+POpfvIj9qanZtn3h8mWsS0gomZN0Ii7ffgvp++9heOi5ISIqLgwVRER2ehAovnUZjhvKHwq1\nrU4Wh93qIUUfEWrRojI7ItTk2FjUdnHBzlq18H2dOki1WvHy1auw3n8eqiuVuNKwoe2xvVatXPeT\narHg/6KiUMfFBZH16mG4tzeG3LyJSJ0OALD43j0kWyw4EhyMcF9fDLp507bt+sREWAEM9fEp8fN1\nBq7Ll8MSGQnz/eeGiKi4MFQQEdkhq1P2dRxwCcMt5U9F2scdxWkcUc1Egv5WobaTy+VwbdgQGRER\nZXJEqP9ptZhbrRqe1mjQys0NSwID8bfRiL/vX61QSBJqubig9v1HQB7haV1iIgSAT7RaPKFWY3rV\nqmim0WDl/asPJzMyMN7XFyFqNUZVqQIASMjMRFJmJqbfvo2V1auXyvk6AwmAZtYs6G/cKNTVMSKi\nx2GoICKnYLFYivwlZ+fOndi/f38xl+jxhBBI0N/CMcW8Ajd5ystF1Zf4XfoCqcbCNcN5MCKUceRI\nu47vCFUUimzTbrKsjyRrITsTH05PRzt3dygkyTavvYcHjt//NV6rVOL71FRYhMBP6emQA/BRKDD1\n9m2EVq6Mxmq1fSdShghJgmHMGCi8vCA99HwREdmLoYKIikVERARkMpntIZfL0b9//3y3SU5OxqZN\nm9C/f3/4+voiLpdOpDt27ECjRo2gVqvRvHlznD59Ottyg8GA0aNH4969e8V6PgWRYryLs9JanHP5\ntFj2d8R1Gq6bfyr8iFA+PrD06AHT888XSzkc5avkZGiVSjRydQUA3DCZ4H6/H0RYTAxS8+jQftVo\nRC0Xl2zzaiiViDGbAQBT/f1xIC0NqnPn8PK1a/ifVotInQ57U1Mxu2rVkj0pJyJcXaH78EPIuneH\nplo1hgoiKlYMFURUbJ599llERUXhypUruHz5MpYuXZrv+sOGDcPkyZORkJCAlJSUHMt//fVXhIaG\nYvTo0YiMjIRWq0WXLl2QkZFhW2fFihW4e/cuhg0bBpVKBaVSCYVCAblcDpVKZZunUqmwZMmSYjvX\ndGMS/rLuws+us4ttn5AEdrn1R6zhXOFHhAoIgHHCBFhq1y6+8pSi3/V6zL97F0sCAyFJEjp7eOB0\n/fo4FhyMt/38sCkpCQNv3Mh123SrFZpHviBrZDIY7l/xqKFS4WLDhohu3BjxTzyBzp6eePPWLSwO\nDMSu1FQ0/usvNLh4ERvLcQdmi78/0j/5BC4tWsClcmVHF4eIyiHF41chIioYtVqNWnl0ps3Nxx9/\njICAABw5cgQ//JCzg/PixYvx0ksvYdy4cQCAtWvXomrVqti2bRuGDBmC8+fPIyIiAkeOHEHr1q1t\n261duxbffPMNdu3aZf9J5UJv1uGq+Sj2ub1hu1N2ccmU9PjKrTf66fYjwK0hZLKC/fYjSRLcataE\nbt48uL3xBmTJycVbsBJ0y2RC16tXMb5KFfS4/4XXT6mE3/0+FCFqNSrJ5eh3/TrizGZUfaRvhYtM\nBtMjTaYMQuQIGg/6ZCy7dw9VFAo8pVaj69Wr+Dk4GDqLBS0vX0YHDw9UK6Md3/OSGRIC/YwZ0NSq\nBblc7ujiEFE5xSsVROQwAQEB+S4/dOgQOnXqZJuuVKkSmjRpguPHjyM1NRW9e/fGjBkzsgUKACXa\nAdViseCW4Qx2uvWFkErmOOmyWHyrHoK7GdcKPyJUnTplakSoO2YzXoyKwr89PDA/n9dDiKsrBIDY\n+02aHhaoVCL6kfnRJhNqP9IkCgDizGa8f+cOPq5eHQfS0vCypyd8FQoEubigrbs7Tj50Faw8MPbo\nAcN778G9Th0GCiIqUQwVRFRsjh49Cnd3dzzxxBN47733YDKZiryv5ORkJCcn57jyUaNGDcTExMDd\n3R2TJk1Cx44d4e7uDk9PT9tj3Lhx2Lt3r23aw8MDnp6eOHHihF3nJ4TA7Yy/8Y1bH1gko137epw4\nxSn8pJpd+BGhFAq4NGyIjHffdfoRoRIyM/FiVBSe1WiwpkaNfNeNzMiAHEBwLkHhOTc3/JiWli2A\nHUxPRwd39xzrToqNxegqVVDHxQUGqzXbFQ6d1QplOelnIGQyZEyZAuuoUXDTatl/gohKHEMFERWL\n4cOH48yZMzh69ChGjBiBRYsWYdKkSUXeX3p6OgBAo9Fkm6/RaGAwGCCTyTBq1CgIIeDn54fU1FTb\nY/ny5ejUqZNtOi0tDampqXj22WeLXJ4HQ8fuc30TOtntIu+nMC6oNuKC9CXSCjkilMrNDfJnn4Vx\n2LASKpn9Ui0W/DsqCj5yOab7+yPKaLQ9LELg/bg4bE9Oxu96PdYnJiI8NhZjqlSBx/1f2/tfv44l\nd+8CAF7z8UGyxYIxt27hgl6POXFx+EOvx3hf32zH/DEtDSd0Okz18wMAtHZzw5bkZBxMS8OO5GSc\nzMhAi0deb2WRcHODbulSyDt3hrpqVQYKIioV7FNBRMVCq9VCq9UCAJo0aYLMzEzMnj0bH330EYKD\ng3H9+nUAWW3/o6KibOvmxeX+L9KPXu0wGAw5gkZpSDUm4Kx8LaKVR0r1uIdcp8BbVx/1ZP+Gi7Lg\nQ5+6+Pggo1cvmK9cgfKnot0/oySd0etxTp81ylXDv/4CAAhkdVG51qgRVDIZxt+6hWSLBUEqFSb6\n+mLS/TAAAH8ZjfC/38SrmlKJ3bVrY+ytW/gsMRGNXV2xp04daFUq2/pmITAuJgbLq1eH6n4/leZu\nbgjz9UXojRuoJJPh8xo14KUo2x+LloAAZHzwAdTBwVDkclWHiKiklO13TyJyWiEhITAYDEhMTMT+\n/fthfqjN++P6UgBAlSpV4OLigujo6Gzzo6Oj8cwzz9imhRA5+h0Uph9CQZgyjbiZ+St+cZtbrPst\nEElgp1so+qcfQk1Z0wK3i5ckCZrAQKSHhUEWHQ35/VDnLJ53d4flqafyXP62nx/efihEPOp0/frZ\nptu4u+NcgwZ5rq+UJFzIZfkUf39M8fcvQImdn7lpUximToVbrVoF7uBPRFRcGCqIqERERkbCx8cH\n3t7e8Pb2LvT2kiShZcuWOHDgAAYPHgwASElJwalTpzBlyhRs2LABw4cPhyRJsFgsUD30q/SDoPHo\nPEmSkJCQAA8PjwKXw2q14rb+Ina7Dyj2kZ4KKlPKwFduvdBPtx+B7g0L3JzFNiLU/PlwGzUKstTU\nEi4pOYqxTx+YBw6Ee/XqbO5ERA7BnzKIqFi8/fbb+O6773D+/Hl89NFHWLBgAaZNm5bvNvfu3UNU\nVBRu3boFIQSuXbuGqKgoW3+KsLAwbNmyBZ988gnOnz+P4cOHo0GDBujSpQsGDRoEs9kMk8kEi8UC\nk8lke6xatQpdunTJNu/BuoUJFEII3M24il2a/jBJaXY9P/ZKl8XgW/VQ3NFdLfSIUOq6daFbvBii\njDftoZyETIaM6dNhHTECbgwURORADBVEVCzMZjOGDx+OVq1aYe3atVi6dCneeuutfLeZPHkygoOD\nMXjwYEiShLZt26JevXr46quvAADdunXD0qVLMWfOHLRq1QqZmZnYvXt3qX1xSjMl4rRiNRLkF0vl\neI8TpziJn5XvIVEfU6jtFA9GhJo1y+lHhKKCs7q7Q7d8ORT/939Q+/kxUBCRQ0miuBsfExE52Jo1\na7Bz5067bn5nsVgQpf8Jm9zaA5JzvU221/8HT8uGwsOlcM3KDPHxwMaNcF2/voRKRqXFotUiY8GC\nrA7ZJXhPkgfNBomIHodXKoiIHiGEwJ2MK9itHuR0gQIAfnSdhBvmX2A0Gwq1nYuPDyyvvALzIzcL\npLLF3LIlMpYsgVuDBiUWKIQQSNXpcC8pOdsgC0REeeGVCiKiR6QY4nFEmo3TLh87uih5Ugo3hKb/\niJqago8IBWR9WUy/fh2asDDIb9wowRJScRMAjIMGwdKnDzSBgSV2BUEIgcTUNFxLNSJGn4nmPq7w\n96rEEaWIKF8MFURED8lq9vQzNrm1c8qrFA/zsAaibyFHhAKyRrTS/f033N54gyNClRFCoUDGzJmQ\ntWwJ1ypVSixQWK1WxKek4s9kIxJNVgCAWi7hGR81qlT2ZFMoIsoTf3YgIrpPCIG7+qv4Xj3c6QMF\nAKTJYvCdejjuFnFEqIxFizgiVBlg9fRE+ooVULZvD7Wvb4l9sTebzbiTlIJTCQZboAAAvUXgrxQD\nktPSS+S4RFQ+MFQQEd1nMGfgT/kWJMuvOrooBXZbcQI/K+cWfkQopRKqRo2QMWMGR4RyYpagIOhW\nr4b66aehcncvseNkGAyITU5DZIIBekvOV8Q9oxW30o3Q6QvXj4eIKg6GCiIiZF2liDdew6+u8xxd\nlEL73WUd/sR2pBmTCrWdyt0dslatYBw0qIRKRvYwtW2LjA8/hFu9elCU0BUlIQRS0nW4kaTDqUQj\ncskTNpfTzEhIz4DFYimRshBR2cZQQUQEINUYj+OqD5Ap6R1dlCI5qA7PGhEqs3C/JLtWqQJLnz4w\nt2xZQiWjwhIADMOHwzx5MtyDgkqsg/SDDtl/J+pwMdVUoG3+SDYhMS29UM3tiKhiYKggogrParUi\n3hyFC6ovHF2UopMEdrr1Q6z+j0L9kixJEjSBgTBMmgSLVluCBaSCEEolMubNA0JDoQk9VIi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PSYJRThcJjSqmqWl/up0Jvfr15NgeOy3GSk1C8pFUI0DZJUCCEOKDk5me+//56ePXvu9/0TTjiB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\nDxBNeHlQaQNkAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xf09f470>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#-*-数据可视化-*-\n",
    "matplotlib.style.use('ggplot')\n",
    "fig2 = plt.figure(2,facecolor = 'white',figsize=((8,6)))\n",
    "ax2_1 = fig2.add_subplot(2,1,1)\n",
    "plt.tick_params(colors='black')\n",
    "df_experience.value_counts().plot(kind = 'bar',rot = 0,color='#7EC0EE',width=0.3)\n",
    "title = plt.title('工作经验—职位数分布图',fontsize =12,color = 'black')\n",
    "xlabel = plt.xlabel('工作经验',fontsize =10,color = 'black')\n",
    "ylabel = plt.ylabel('职位数量',fontsize =10,color = 'black')\n",
    "#设置说明，位置在图的右上角\n",
    "text1=ax2_1.text(4.5,2200,'工作经验样本数:6433(个)',fontsize=10,color='black')\n",
    "plt.grid(True)\n",
    "ax2_2 = fig2.add_subplot(2,1,2)\n",
    "x2 = df_experience.value_counts().values\n",
    "labels = list(df_experience.value_counts().index[:5])+['']*2\n",
    "explode = tuple([0.1,0.1,0.1,0.1,0.1,0.08,0.08])\n",
    "colors='#FF8247','#ADD8E6','#FF3030','#7FFF00','#C67171'\n",
    "#参数autopct='%1.1f%%'来显示饼图中每一块的比例\n",
    "plt.pie(x2,explode=explode,labels=labels,autopct='%1.1f%%',colors=colors,textprops={'color':'black'},startangle=180)\n",
    "#显示为等比例圆形\n",
    "plt.axis('equal')\n",
    "#设置图例，方位为右下角\n",
    "legend = ax2_2.legend(loc='lower right',shadow=False,fontsize=8)\n",
    "#更改图例背景颜色\n",
    "frame=legend.get_frame()\n",
    "frame.set_facecolor('gray')\n",
    "#布局自动调整\n",
    "fig2.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6060935799782372"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#上图对工作经验的要求，'不限'+'无经验'+'1年以下'+'1-3年'占比超过60%，说明企业对机器学习岗位相关工作经验不是很丰富的求职者提供了一些工作机会\n",
    "d=df_experience.value_counts()\n",
    "(d['不限']+d['无经验']+d['1年以下']+d['1-3年'])/d.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#新增1列df_experience到数据表中\n",
    "df['df_experience']=df_experience"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "面议         590\n",
       "12500.5    575\n",
       "17500.5    500\n",
       "25000.5    431\n",
       "15000.0    429\n",
       "22500.0    388\n",
       "30000.0    290\n",
       "20000.0    289\n",
       "40000.5    267\n",
       "9000.5     227\n",
       "5000.5     202\n",
       "7000.5     190\n",
       "11500.0    177\n",
       "无内容        122\n",
       "9000.0     113\n",
       "3000.5     106\n",
       "12000.0    101\n",
       "37500.0     91\n",
       "27500.0     80\n",
       "12500.0     71\n",
       "7500.0      64\n",
       "10000.0     62\n",
       "8000.0      61\n",
       "25000.0     60\n",
       "6000.0      55\n",
       "16000.0     52\n",
       "10500.0     49\n",
       "17500.0     49\n",
       "18000.0     46\n",
       "32500.0     42\n",
       "          ... \n",
       "8250.0       1\n",
       "35500.0      1\n",
       "2600.0       1\n",
       "43500.0      1\n",
       "8999.5       1\n",
       "30500.0      1\n",
       "3850.0       1\n",
       "23333.5      1\n",
       "11750.0      1\n",
       "4600.0       1\n",
       "46000.0      1\n",
       "10750.0      1\n",
       "26250.0      1\n",
       "20000.5      1\n",
       "3600.0       1\n",
       "12499.5      1\n",
       "17150.0      1\n",
       "45000.5      1\n",
       "36000.0      1\n",
       "53000.0      1\n",
       "9750.0       1\n",
       "2750.0       1\n",
       "70000.0      1\n",
       "37499.5      1\n",
       "2000.0       1\n",
       "29000.0      1\n",
       "76500.0      1\n",
       "44000.0      1\n",
       "5650.0       1\n",
       "11999.5      1\n",
       "Name: average, dtype: int64"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#3.工作经验对于收入的影响情况分析\n",
    "#统计数据表中平均月薪列的个数\n",
    "df.average.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#把平均月薪列中显示\"面议\"与\"无内容\"的项替换成空值\n",
    "df_average = df['average'].replace(['面议','无内容'],np.nan)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#按工作经验与平均月薪列组成新的数据表df_exp_ave，得到不同工作经验字段下的平均月薪，\n",
    "df_exp_ave=pd.DataFrame(data={'工作经验':df['df_experience'],'平均月薪':df_average})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 6555 entries, 16122 to 22896\n",
      "Data columns (total 2 columns):\n",
      "工作经验    6433 non-null object\n",
      "平均月薪    5843 non-null object\n",
      "dtypes: object(2)\n",
      "memory usage: 153.6+ KB\n"
     ]
    }
   ],
   "source": [
    "#查看数据表df_exp_ave相关信息\n",
    "df_exp_ave.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [],
   "source": [
    "#将数据表df中平均月薪列的类型由object类型转换成float类型\n",
    "import re\n",
    "pattern = re.compile('([0-9]+)')\n",
    "#存放转换后的平均月薪数据\n",
    "listi = []\n",
    "for i in range(len(df.average)):\n",
    "    item = df.average.iloc[i].strip()\n",
    "    result = re.findall(pattern,item)\n",
    "    try:\n",
    "        if result:\n",
    "            listi.append(float(result[0]))\n",
    "        elif (item.strip()=='无内容' or item.strip()=='面议'):\n",
    "            listi.append(np.nan)\n",
    "        else:\n",
    "            print(item)\n",
    "    except Exception as e:\n",
    "        print(item,type(item),repr(e))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#更新当前数据表df_exp_ave与数据表df中的平均月薪列\n",
    "df_exp_ave['平均月薪'] = listi\n",
    "df['df_average'] = df_exp_ave['平均月薪']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "12500.0    646\n",
       "17500.0    549\n",
       "25000.0    491\n",
       "15000.0    429\n",
       "22500.0    390\n",
       "9000.0     340\n",
       "40000.0    297\n",
       "30000.0    290\n",
       "20000.0    290\n",
       "5000.0     211\n",
       "7000.0     207\n",
       "11500.0    177\n",
       "3000.0     109\n",
       "12000.0    101\n",
       "37500.0     91\n",
       "27500.0     80\n",
       "7500.0      64\n",
       "10000.0     62\n",
       "8000.0      61\n",
       "6000.0      55\n",
       "16000.0     52\n",
       "10500.0     49\n",
       "18000.0     46\n",
       "60000.0     44\n",
       "32500.0     42\n",
       "14000.0     41\n",
       "45000.0     38\n",
       "24000.0     34\n",
       "6500.0      33\n",
       "13500.0     32\n",
       "          ... \n",
       "17150.0      1\n",
       "26250.0      1\n",
       "9750.0       1\n",
       "11750.0      1\n",
       "2000.0       1\n",
       "43500.0      1\n",
       "23333.0      1\n",
       "6750.0       1\n",
       "22498.0      1\n",
       "30500.0      1\n",
       "5650.0       1\n",
       "46000.0      1\n",
       "4750.0       1\n",
       "37499.0      1\n",
       "4600.0       1\n",
       "70000.0      1\n",
       "29000.0      1\n",
       "12499.0      1\n",
       "3850.0       1\n",
       "53000.0      1\n",
       "11999.0      1\n",
       "3600.0       1\n",
       "36000.0      1\n",
       "44000.0      1\n",
       "76500.0      1\n",
       "2750.0       1\n",
       "35500.0      1\n",
       "2600.0       1\n",
       "10750.0      1\n",
       "8250.0       1\n",
       "Name: 平均月薪, dtype: int64"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#统计每个平均月薪字段的个数\n",
    "df_exp_ave['平均月薪'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5843"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#查看数据表df_exp_ave中平均月薪样本总数\n",
    "df_exp_ave['平均月薪'].value_counts().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "18786.41485538251"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#查看数据表df_exp_ave中平均月薪样本总数平均值\n",
    "df_exp_ave['平均月薪'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#针对平均月薪列按工作经验进行分组\n",
    "exp_ave_group = df_exp_ave['平均月薪'].groupby(df_exp_ave['工作经验'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "工作经验\n",
       "1-3年     16623.986230\n",
       "10年以上    52948.717949\n",
       "1年以下     11491.935484\n",
       "3-5年     21428.644168\n",
       "5-10年    28966.524691\n",
       "不限       15837.190824\n",
       "无经验       8012.096774\n",
       "Name: 平均月薪, dtype: float64"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#查看分组后工作经验列中各个字段平均值\n",
    "exp_ave_group.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "工作经验\n",
       "10年以上    52948.7\n",
       "5-10年    28966.5\n",
       "3-5年     21428.6\n",
       "平均值      18786.4\n",
       "1-3年     16624.0\n",
       "不限       15837.2\n",
       "1年以下     11491.9\n",
       "无经验       8012.1\n",
       "dtype: float64"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#新增一项数据表df_exp_ave['平均月薪']的平均值\n",
    "ave1 = pd.Series(data = {'平均值':df_exp_ave['平均月薪'].mean()})\n",
    "result1 = exp_ave_group.mean().append(ave1)\n",
    "#sort_values()方法可以对值进行排序，默认按照升序，round（1）表示小数点后保留1位小数。\n",
    "result1.sort_values(ascending=False).round(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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9+OgzPap48eJYLBbu379vtX/BggXUqlULNze3JK+zYMECxo4dS5cuXahUqRIV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      "text/plain": [
       "<matplotlib.figure.Figure at 0xd0f77b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#-*-数据可视化-*-\n",
    "matplotlib.style.use('ggplot')\n",
    "fig3 = plt.figure(3,facecolor = 'white',figsize=((15,6)))\n",
    "ax3 = fig3.add_subplot(1,1,1)\n",
    "result1.sort_values(ascending=False).round(1).plot(kind='barh',rot=0,color='#7EC0EE')\n",
    "#设置标题、x轴、y轴的标签文本\n",
    "title = plt.title('工作经验—平均月薪分布图',fontsize = 12,color = 'black')\n",
    "xlabel= plt.xlabel('平均月薪',fontsize = 10,color = 'black')\n",
    "ylabel = plt.ylabel('工作经验',fontsize = 10,color = 'black')\n",
    "#添加值标签\n",
    "list1 = result1.sort_values(ascending=False).values\n",
    "for i in range(len(list1)):\n",
    "    ax3.text(list1[i],i,str(int(list1[i])),color='black')\n",
    "#设置标识箭头\n",
    "arrow = plt.annotate('机器学习平均月薪:18786元/月', xy=(18786,3.25), xytext=(21000,3.65),color='black',fontsize=12,arrowprops=dict(facecolor='black', shrink=0.05))\n",
    "#设置图例注释（5843来源：df_exp_ave[u'平均月薪'].value_counts().sum()）\n",
    "text= ax3.text(50000,7,'月薪样本数:5843(个)',fontsize=12, color='black')\n",
    "#设置轴刻度文字颜色为黑色\n",
    "plt.tick_params(colors='black')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#上图进一步说明了在机器学习相关领域工作经验越多，平均月薪越高"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#4.工作城市对于收入的影响情况分析\n",
    "#按'df_city'与'df_average'组成新的数据表df_city_ave\n",
    "df_city_ave=pd.DataFrame(data={'工作城市':df['df_city'],'平均月薪':df['df_average']})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 6555 entries, 16122 to 22896\n",
      "Data columns (total 2 columns):\n",
      "工作城市    6433 non-null object\n",
      "平均月薪    5843 non-null float64\n",
      "dtypes: float64(1), object(1)\n",
      "memory usage: 153.6+ KB\n"
     ]
    }
   ],
   "source": [
    "#查看数据表df_city_ave相关信息\n",
    "df_city_ave.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#针对平均月薪列按工作城市进行分组\n",
    "city_ave_group = df_city_ave['平均月薪'].groupby(df_city_ave['工作城市'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "工作城市\n",
       "上海      21053.473616\n",
       "佛山      12836.206897\n",
       "北京      23615.836865\n",
       "南京      14753.205128\n",
       "南宁      10578.125000\n",
       "南昌       8000.000000\n",
       "厦门      15652.941176\n",
       "合肥      12702.336449\n",
       "呼和浩特     7600.000000\n",
       "大连      14077.272727\n",
       "天津      11615.000000\n",
       "太原       6730.000000\n",
       "宁波      12576.923077\n",
       "广州      16233.035336\n",
       "成都      14901.328000\n",
       "无锡       9954.166667\n",
       "昆明      10125.000000\n",
       "杭州      19109.229730\n",
       "武汉      11939.567708\n",
       "济南      10432.882883\n",
       "海口       9000.000000\n",
       "深圳      19885.301852\n",
       "石家庄      8151.515152\n",
       "福州      15019.480519\n",
       "苏州      15172.764228\n",
       "西安      12322.784810\n",
       "贵阳      10206.896552\n",
       "郑州      13974.468085\n",
       "重庆      12172.727273\n",
       "长沙      12115.000000\n",
       "青岛      11000.000000\n",
       "Name: 平均月薪, dtype: float64"
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#查看对31个城市分组后，每个城市月薪的平均值\n",
    "city_ave_group.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5843"
      ]
     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#查看对31个城市分组后，筛选出的平均月薪样本数\n",
    "city_ave_group.count().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "工作城市\n",
       "北京      23615.8\n",
       "上海      21053.5\n",
       "深圳      19885.3\n",
       "杭州      19109.2\n",
       "平均值     18786.4\n",
       "广州      16233.0\n",
       "厦门      15652.9\n",
       "苏州      15172.8\n",
       "福州      15019.5\n",
       "成都      14901.3\n",
       "南京      14753.2\n",
       "大连      14077.3\n",
       "郑州      13974.5\n",
       "佛山      12836.2\n",
       "合肥      12702.3\n",
       "宁波      12576.9\n",
       "西安      12322.8\n",
       "重庆      12172.7\n",
       "长沙      12115.0\n",
       "武汉      11939.6\n",
       "天津      11615.0\n",
       "青岛      11000.0\n",
       "南宁      10578.1\n",
       "济南      10432.9\n",
       "贵阳      10206.9\n",
       "昆明      10125.0\n",
       "无锡       9954.2\n",
       "海口       9000.0\n",
       "石家庄      8151.5\n",
       "南昌       8000.0\n",
       "呼和浩特     7600.0\n",
       "太原       6730.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#新增一个df_city_ave['平均月薪']的平均值\n",
    "ave2 = pd.Series(data = {'平均值':df_city_ave['平均月薪'].mean()})\n",
    "result2 = city_ave_group.mean().append(ave2)\n",
    "#对平均月薪值进行降序排列\n",
    "result2.sort_values(ascending=False).round(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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lxowhMWaMzBAjZGycRkZG5MyZM3VjM2THf0lERASNGzfG0tJSqzecyMbGhunT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Dk5MWnSJE6dOkWlSpXo1KkTr7/+OqVKlQJgxYoVZGRk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\nkbvvXuqbLCLF557+HnzppZfYtWuX8bpevXoAfPfddwwaNIgzZ84QEhJCZ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tLU2RkZEaN26cXn311YD8IiQkRKVKlVKHDh3k8XjMzxwREaH3338/YH+Vm7+y/TGO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\nb2ywaNEi7dmzJ9f3Nd6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8/ODl5YW9e/fy4Gu1atUwd+5cPgNMJBJh165dWL58OR9AT05O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\nAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x133a3518>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#-*-数据可视化-*-\n",
    "matplotlib.style.use('ggplot')\n",
    "fig4 = plt.figure(4,facecolor = 'white',figsize=((17,8)))\n",
    "ax4 = fig4.add_subplot(1,1,1)\n",
    "result2.sort_values(ascending=False).round(1).plot(kind='bar',rot=0.2,color='#7EC0EE')\n",
    "#设置图标题，x和y轴标题\n",
    "title = plt.title('城市—平均月薪分布图',fontsize=15,color='black')\n",
    "xlabel = plt.xlabel('城市',fontsize=14,color='black')\n",
    "ylabel = plt.ylabel('平均月薪',fontsize=14,color='black')\n",
    "#设置说明，位置在图的右上角\n",
    "text1 = ax4.text(25,24000,'城市总数:31(个)',fontsize=14, color='black')\n",
    "text2 = ax4.text(25,22800,'平均月薪样本数:5843(个)',fontsize=14, color='black')\n",
    "#添加每一个城市的坐标值\n",
    "list2 = result2.sort_values(ascending=False).values\n",
    "for i in range(len(list2)):\n",
    "    ax4.text(i-0.5,list2[i],int(list2[i]),color='black')\n",
    "#设置箭头注释\n",
    "arrow = plt.annotate('全国月薪平均值:18786元/月', xy=(4.5,18786), xytext=(7,20000),color='black',fontsize=14,arrowprops=dict(facecolor='black', shrink=0.05))\n",
    "plt.tick_params(colors='black')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#上图说明北上深杭这4个城市机器学习相关领域的平均月薪高于全国平均月薪"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "本科     3830\n",
       "硕士     1184\n",
       "不限      713\n",
       "大专      514\n",
       "无内容     122\n",
       "博士      105\n",
       "中专       16\n",
       "中技        8\n",
       "初中        2\n",
       "其他        1\n",
       "Name: 最低学历, dtype: int64"
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#5.招聘数量与学历的关联情况分析\n",
    "#统计数据表'最低学历'的职位数\n",
    "df['最低学历'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#处理干扰数据\n",
    "df_degree=df['最低学历'].replace(['无内容','中专','中技','初中','其他'],np.nan)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "本科    3830\n",
       "硕士    1184\n",
       "不限     713\n",
       "大专     514\n",
       "博士     105\n",
       "Name: 最低学历, dtype: int64"
      ]
     },
     "execution_count": 130,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#查看处理后的信息\n",
    "df_degree.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6346"
      ]
     },
     "execution_count": 131,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#统计处理后的职位样本数\n",
    "df_degree.value_counts().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#新增1列'df_degree'到数据表中\n",
    "df['df_degree']=df_degree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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cHURcbCxPFS5Mg8YvMOC9IaTlcyMN8KnyHJNmzGFC0AimT5/BM9Wr83Xw9+Ba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I83vxPjkNrbKPn1ZW6H0muYtbX4nnqKZG2t26GI9Ho96x1XRHoMx3FIVqzG/9kLbocifYzh8eB4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\nGCiIiIiIiGjI/j+IIko1X7qoUgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xdf1fdd8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#-*-数据可视化-*-\n",
    "matplotlib.style.use('ggplot')\n",
    "fig5 = plt.figure(5,facecolor = 'white',figsize=((8,8)))\n",
    "ax5_1 = fig5.add_subplot(2,1,1)\n",
    "df_degree.value_counts().plot(kind = 'bar',rot=0,width=0.3,color='#7EC0EE')\n",
    "#设置标题、x轴和y轴标题、图例文字\n",
    "title = plt.title('最低学历—职位数分布图',fontsize = 12,color = 'black')\n",
    "xlabel = plt.xlabel('最低学历',fontsize = 10,color = 'black')\n",
    "ylabel = plt.ylabel('职位数量',fontsize = 10,color = 'black')\n",
    "text1 = ax5_1.text(3.5,3600,'职位样本数:6346(个)',fontsize=10, color='black')\n",
    "#设置坐标轴的的颜色和文字大小\n",
    "plt.tick_params(colors='black',labelsize=8)\n",
    "#设置坐标值文字\n",
    "list5 = df_degree.value_counts().values\n",
    "for i in range(len(list5)):\n",
    "    ax5_1.text(i-0.1,list5[i],int(list5[i]),color='black')\n",
    "ax5_2=fig5.add_subplot(2,1,2)\n",
    "xl = df_degree.value_counts().values\n",
    "labels = list(df_degree.value_counts().index)\n",
    "explode = tuple([0.1,0,0,0,0])\n",
    "colors='#FF8247','#ADD8E6','#FF3030','#7FFF00','#C67171'\n",
    "plt.pie(xl,explode=explode,labels=labels,colors=colors,autopct='%1.1f%%',textprops={'color':'black'},startangle=90)\n",
    "plt.axis('equal')\n",
    "legend = ax5_2.legend(loc='lower right',shadow=False,fontsize=7)\n",
    "frame=legend.get_frame()\n",
    "frame.set_facecolor('gray')\n",
    "plt.tick_params(colors='black',labelsize=13)\n",
    "fig5.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#上图显示企业对最低学历为本科的求职者需求量最大，占比超过60%"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#6.学历对于收入的影响情况分析\n",
    "#按'df_degree'与'df_average'组成新的数据表df_deg_ave\n",
    "df_deg_ave=pd.DataFrame(data={'最低学历':df['df_degree'],'平均月薪':df['df_average']})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 6555 entries, 16122 to 22896\n",
      "Data columns (total 2 columns):\n",
      "平均月薪    5843 non-null float64\n",
      "最低学历    6346 non-null object\n",
      "dtypes: float64(1), object(1)\n",
      "memory usage: 153.6+ KB\n"
     ]
    }
   ],
   "source": [
    "#查看新数据表df_deg_ave相关信息\n",
    "df_deg_ave.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "最低学历\n",
       "不限    16724.181548\n",
       "博士    26740.737374\n",
       "大专    11719.466403\n",
       "本科    19211.439685\n",
       "硕士    21509.836184\n",
       "Name: 平均月薪, dtype: float64"
      ]
     },
     "execution_count": 137,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#按'最低学历'对'平均月薪'进行分组，并计算分组后的均值\n",
    "deg_ave_group = df_deg_ave['平均月薪'].groupby(df_deg_ave['最低学历'])\n",
    "deg_ave_group.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5820"
      ]
     },
     "execution_count": 138,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#统计新数据表月薪样本数\n",
    "deg_ave_group.count().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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8mO7du2Nra8ulS5cYPXo0jo6OWFlZMXz4cMaNG0eVKlUoXrw4w4cPx8HBgfLl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\nIiIiIk4oYBYRERERcUIBs4iIiIiIE/8H8RJQ3e31ctAAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xdf99d68>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#-*-数据可视化-*-\n",
    "matplotlib.style.use('ggplot')\n",
    "fig6 = plt.figure(6,facecolor = 'white',figsize=((8,4)))\n",
    "ax6 = fig6.add_subplot(1,1,1)\n",
    "#在条形图上叠加一个折线图\n",
    "deg_ave_group.mean().round(1).sort_values().plot(color = '#EE5C42')\n",
    "deg_ave_group.mean().round(1).sort_values().plot(kind='bar',rot=0,width=0.3,color='#7EC0EE')\n",
    "#设置标题、x轴、y轴的标签文本\n",
    "title = plt.title('最低学历—平均月薪分布图',fontsize = 14,color = 'black')\n",
    "xlabel= plt.xlabel('最低学历',fontsize = 12,color = 'black')\n",
    "ylabel = plt.ylabel('平均月薪',fontsize = 12,color = 'black')\n",
    "#添加值标签\n",
    "list6 = deg_ave_group.mean().round(1).sort_values().values\n",
    "for i in range(len(list6)):\n",
    "    ax6.text(i-0.1,list6[i],int(list6[i]),color='black')\n",
    "#设置图例注释\n",
    "text= ax6.text(-0.3,27000,'月薪样本数:5820(个)',fontsize=12, color='black')\n",
    "#设置轴刻度的文字颜色\n",
    "plt.tick_params(colors='black')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#上图进一步说明学历越高，平均月薪越高"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#7.学历与工作经验对于收入的影响情况分析\n",
    "#按'df_degree'、'df_experience'与'df_average'组成新的数据表df_deg_exp_ave\n",
    "df_deg_exp_ave = pd.DataFrame(data = {'平均月薪':df['df_average'],'最低学历':df['df_degree'],'工作经验':df['df_experience']})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 6555 entries, 16122 to 22896\n",
      "Data columns (total 3 columns):\n",
      "工作经验    6433 non-null object\n",
      "平均月薪    5843 non-null float64\n",
      "最低学历    6346 non-null object\n",
      "dtypes: float64(1), object(2)\n",
      "memory usage: 204.8+ KB\n"
     ]
    }
   ],
   "source": [
    "#查看数据表df_deg_exp_ave信息\n",
    "df_deg_exp_ave.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [],
   "source": [
    "#按'最低学历'与'工作经验'的组合对'平均月薪'进行分组\n",
    "deg_exp_ave_group = df_deg_exp_ave['平均月薪'].groupby([df_deg_exp_ave['最低学历'],df_deg_exp_ave['工作经验']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "最低学历  工作经验 \n",
       "不限    1-3年     14070.8\n",
       "      1年以下      5000.0\n",
       "      3-5年     17900.0\n",
       "      5-10年    29733.3\n",
       "      不限       16569.4\n",
       "      无经验      15406.2\n",
       "博士    1-3年     23733.3\n",
       "      10年以上    32500.0\n",
       "      1年以下     21250.0\n",
       "      3-5年     35750.0\n",
       "      5-10年    50000.0\n",
       "      不限       25005.8\n",
       "      无经验       5900.0\n",
       "大专    1-3年     10981.2\n",
       "      10年以上    25000.0\n",
       "      1年以下      6794.1\n",
       "      3-5年     15654.1\n",
       "      5-10年    29386.4\n",
       "      不限        7710.7\n",
       "      无经验       4388.9\n",
       "本科    1-3年     16285.0\n",
       "      10年以上    52685.2\n",
       "      1年以下      8900.0\n",
       "      3-5年     21164.7\n",
       "      5-10年    27362.8\n",
       "      不限       15800.0\n",
       "      无经验       6978.1\n",
       "硕士    1-3年     20679.7\n",
       "      10年以上    61388.9\n",
       "      1年以下     15884.6\n",
       "      3-5年     26026.2\n",
       "      5-10年    34071.4\n",
       "      不限       17386.8\n",
       "      无经验       9107.1\n",
       "Name: 平均月薪, dtype: float64"
      ]
     },
     "execution_count": 144,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#统计分组后的均值\n",
    "deg_exp_ave_group.mean().round(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "最低学历  工作经验 \n",
       "不限    1-3年       53\n",
       "      1年以下        1\n",
       "      3-5年       45\n",
       "      5-10年      15\n",
       "      不限        550\n",
       "      无经验         8\n",
       "博士    1-3年       15\n",
       "      10年以上       2\n",
       "      1年以下        2\n",
       "      3-5年       12\n",
       "      5-10年       6\n",
       "      不限         57\n",
       "      无经验         5\n",
       "大专    1-3年      160\n",
       "      10年以上       1\n",
       "      1年以下       17\n",
       "      3-5年      133\n",
       "      5-10年      22\n",
       "      不限        164\n",
       "      无经验         9\n",
       "本科    1-3年      985\n",
       "      10年以上      27\n",
       "      1年以下       15\n",
       "      3-5年     1247\n",
       "      5-10年     359\n",
       "      不限        742\n",
       "      无经验        57\n",
       "硕士    1-3年      312\n",
       "      10年以上       9\n",
       "      1年以下       26\n",
       "      3-5年      248\n",
       "      5-10年      84\n",
       "      不限        390\n",
       "      无经验        42\n",
       "Name: 平均月薪, dtype: int64"
      ]
     },
     "execution_count": 145,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#统计不同学历下不同工作经验的职位数\n",
    "deg_exp_ave_group.count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5820"
      ]
     },
     "execution_count": 146,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#统计分组后的职位样本数\n",
    "deg_exp_ave_group.count().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "最低学历\n",
       "不限    14070.8\n",
       "博士    23733.3\n",
       "大专    10981.2\n",
       "本科    16285.0\n",
       "硕士    20679.7\n",
       "Name: 平均月薪, dtype: float64"
      ]
     },
     "execution_count": 147,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#使用层次化索引对分组后的数据进行检索\n",
    "deg_exp_ave_group.mean().round(1)[:,'1-3年']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "最低学历\n",
       "大专    10981.2\n",
       "不限    14070.8\n",
       "本科    16285.0\n",
       "硕士    20679.7\n",
       "博士    23733.3\n",
       "Name: 平均月薪, dtype: float64"
      ]
     },
     "execution_count": 148,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#对检索出来的数据进行排序\n",
    "deg_exp_ave_group.mean().round(1)[:,'1-3年'].sort_values()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#获得检索数据的索引\n",
    "xlist = list(deg_exp_ave_group.mean().round(1)[:,'1-3年'].sort_values().index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "最低学历\n",
       "大专    10981.2\n",
       "不限    14070.8\n",
       "本科    16285.0\n",
       "硕士    20679.7\n",
       "博士    23733.3\n",
       "Name: 平均月薪, dtype: float64"
      ]
     },
     "execution_count": 150,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#对检索数据进行重新排序索引\n",
    "deg_exp_ave_group.mean().round(1)[:,'1-3年'].reindex(xlist)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 4388.9, 15406.2,  6978.1,  9107.1,  5900. ])"
      ]
     },
     "execution_count": 151,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#获得重新排序索引后的平均月薪值\n",
    "deg_exp_ave_group.mean().round(1)[:,'无经验'].reindex(xlist).values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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smOV5Dh06RPfu3Xnx4gW+vr588cUXWbYp3eTJkylevDhjxoyhTZs2fP755/j4+GBtba3W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\n3Lhx6tSpk95666067X/bbbc5v+7Zs6f279/v/H7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//vWv+uijj1Rc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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xd611710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#-*-数据可视化-*-\n",
    "matplotlib.style.use('ggplot')\n",
    "fig7 = plt.figure(7,facecolor = 'white',figsize=((10,6)))\n",
    "ax7 = fig7.add_subplot(1,1,1)\n",
    "title = plt.title('最低学历-工作经验-平均月薪分布图',fontsize = 14,color = 'black')\n",
    "xlabel = plt.xlabel('最低学历',fontsize = 10,color = 'black')\n",
    "ylabel = plt.ylabel('平均月薪',fontsize = 10,color = 'black')\n",
    "#img1~img7分别表示7种柱状图\n",
    "#ind为x轴宽度，用numpy的array形式表示\n",
    "ind = np.arange(5)\n",
    "#ylist1~7分别是7种柱状图的Y值列表\n",
    "ylist1 = deg_exp_ave_group.mean().round(1)[:,'无经验'].reindex(xlist).values\n",
    "ylist2 = deg_exp_ave_group.mean().round(1)[:,'1年以下'].reindex(xlist).values\n",
    "ylist3 = deg_exp_ave_group.mean().round(1)[:,'不限'].reindex(xlist).values\n",
    "ylist4 = deg_exp_ave_group.mean().round(1)[:,'1-3年'].reindex(xlist).values\n",
    "ylist5 = deg_exp_ave_group.mean().round(1)[:,'3-5年'].reindex(xlist).values\n",
    "ylist6 = deg_exp_ave_group.mean().round(1)[:,'5-10年'].reindex(xlist).values\n",
    "ylist7 = deg_exp_ave_group.mean().round(1)[:,'10年以上'].reindex(xlist).values\n",
    "#柱状图的宽度\n",
    "width = 0.1\n",
    "img1 = ax7.bar(ind,ylist1,width)\n",
    "img2 = ax7.bar(ind+width,ylist2,width,color='#9F79EE')\n",
    "img3 = ax7.bar(ind+width*2,ylist3,width,color='#BFEFFF')\n",
    "img4 = ax7.bar(ind+width*3,ylist4,width,color='#FA8072')\n",
    "img5 = ax7.bar(ind+width*4,ylist5,width,color='#87CEEB')\n",
    "img6 = ax7.bar(ind+width*5,ylist6,width,color='#CD3700')\n",
    "img7 = ax7.bar(ind+width*6,ylist7,width,color='#ADFF2F')\n",
    "#设置X轴文本和位置调整\n",
    "ax7.set_xticklabels(xlist)\n",
    "ax7.set_xticks(ind + width / 2)\n",
    "#设置文字说明\n",
    "text1 = ax7.text(4.05,67000,'职位样本数:5820(个)',fontsize=10, color='black')\n",
    "#设置图例\n",
    "legend=ax7.legend((img1[0],img2[0],img3[0],img4[0],img5[0],img6[0],img7[0]), ('无经验','1年以下','不限','1-3年','3-5年','5-10年','10年以上'),fontsize=9,loc='best',shadow=False)\n",
    "#设置图例背景色\n",
    "frame = legend.get_frame()\n",
    "frame.set_facecolor('gray')\n",
    "#设置栅格\n",
    "plt.grid(True)\n",
    "plt.tick_params(colors='black',labelsize=10)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#上图显示与机器学习相关岗位平均月薪最高的3个组合分别为:[硕士+工作经验10年以上]、[本科+工作经验10年以上]以及[博士+工作经验5-10年]"
   ]
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}
